Techniques for semantic business policy composition

ABSTRACT

Embodiments of the present invention relate to techniques for creating policies. A plurality of objects representative of semantic objects are provided to a user. An arrangement of a subset of the objects, the arrangement representative of a policy, is received. The arrangement is converted to instructions for implementation by an application configured to implement policies. One or more of the objects may include fields and/or controls for specifying criteria of semantic objects represented by the objects.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. Non-Provisional ApplicationNo. 15/492,157, filed Apr. 20, 2017, entitled “TECHNIQUES FOR SEMANTICBUSINESS POLICY COMPOSITION,” which is a continuation of U.S.Non-Provisional Application No. 12/714,206, filed Feb. 26, 2010,entitled “TECHNIQUES FOR SEMANTIC BUSINESS POLICY COMPOSITION,” whichclaims the benefit of U.S. Provisional Patent Application No.61/155,790, filed on Feb. 26, 2009, entitled “TECHNIQUES FOR SEMANTICBUSINESS POLICY COMPOSITION.” Each of these applications is herebyincorporated by reference in its entirety for all purposes.

BACKGROUND

Embodiments of the present invention relate to policies, and morespecifically to techniques for creating business policies.

Businesses often have internal business policies intended to address awide range of issues such as security, privacy, trade secrets, criminalactivity of employees or others with access to the business, and manyothers. These business policies address various aspects of a business,such as purchasing, selling, marketing, and internal administration.Because of the large number of activities occurring during the course ofrunning a business, which may have various entities located in a varietyof geographical locations, it is often impractical to manually monitorall activities in which improper behavior or mistakes may occur.

One approach to implementing business policies has been to monitor andcontrol computer systems used to facilitate a business's activities. Forexample, information regarding various activities, such as sales andpayroll, are often stored in one or more data stores. This informationmay be analyzed to find activity that might be in violation of abusiness policy, such as an item on an invoice or paycheck to anemployee being outside of a specified range, or a particular employeeattempting to access information to which he or she is not entitledaccess.

Typically, analyzing data requires a high level of technical expertiseas the data is often created and stored using a wide variety of businessapplications which often have differing standards and specifications,are often custom built for specific purposes, and often lack ability tocommunicate and share information with one another. Consequently, inorder to enact business policies, the expertise of those familiar withthe business applications to which the business policies are to beimplemented is often required. For instance, in order to analyze datastored in a relational database, a person may have to be able toconstruct a proper SQL statement. Generally, commonly-used applicationstypically require users to model policies in SQL, PL/SQL, or anotherapplication-specific or storage-specific language.

Those making the business policies, however, are often not the samepeople with detailed knowledge of the business' systems to which thepolicies are to be applied. For instance, a person or group of peopledeciding that, to prevent employee fraud, all payments over a specificamount should require approval by an appropriate person, may not haveany understanding how invoice data is stored in the business' systems.Such policy makers would prefer to define policies in terms that theyunderstand, such as “user”, “general ledger”, “organization”, etc., andnot in terms of the applications with which policies will beimplemented, such as “database schema x on host 55.55.55.55”, “FND_USERtable”, and “application Y”. Such policy makers would likely prefer notto take the time necessary to learn the specific application terminologyas their duties typically do not require such technical expertise.

Moreover, because businesses typically use several differentapplications to facilitate their activities, it can be burdensome forpolicy makers to learn specific terminology for several applications.Policy makers would rather prefer that they can use an intuitiveinterface in order to apply familiar terminology to create policies thatmay be applied to a variety of applications, without having to create asimilar policy for each application.

Previous applications for implementing business policies have includedapplications that work with specific business applications, and thatrequire users to have an underlying understanding of the technicaldesign of those business applications. One possible reason for this isthat database runtimes, which are frequently the underlying runtime forbusiness applications, cannot easily share runtime resources acrossinstances; and most solutions to policy modeling have either used singledatabase instances or single database connections to support theirruntime requirements.

BRIEF SUMMARY

The following presents a simplified summary of some embodiments of theinvention in order to provide a basic understanding of the invention.This summary is not an extensive overview of the invention. It is notintended to identify key/critical elements of the invention or todelineate the scope of the invention. Its sole purpose is to presentsome embodiments of the invention in a simplified form as a prelude tothe more detailed description that is presented later.

Embodiments of the present invention provide techniques for defininganalysis for implementation of policies. In one embodiment, a method fordefining an analysis is disclosed. The method may be performed under thecontrol of one or more computer systems configured with executableinstructions. Also, in an embodiment, instructions for performing themethod, or variations thereof, may be included on a computer-readablestorage medium.

In an embodiment, the method includes providing a plurality of objectsto a user through an interface where the objects are representative ofsemantic objects. An arrangement of a subset of the objects may bereceived, where the arrangement is representative of an analysis to beperformed as part of implementation of a policy. The arrangement may bebased at least in part on interaction by the user with the interface.The arrangement may be converted to executable instructions suitable forexecution by an application configured to operate according to theinstructions. For example, executable instructions may be generatedbased at least in part on the arrangement.

At least one of the objects may include a field configured to allowuser-definition of criteria for a semantic object corresponding to theobject. Further, the arrangement may include a first objectrepresentative of a first semantic object and a second objectrepresentative of a second semantic object where the second semanticobject represents an attribute of the first semantic object. Thearrangement of the subset of the objects may include an analytic objectrepresentative of a data analysis technique to be applied duringimplementation of the policy.

In an embodiment, the method may also include providing an analyticobject representative of an analysis to be performed as part ofimplementing the policy. Also, the arrangement may be based at least inpart on an association of the analytic representation with at least asubset of the objects. Also, the method may include receiving a groupingof a plurality of members of the subset of objects and performing auser-specified analysis on data corresponding to the members.

In accordance with another embodiment of the invention, a system forcreating policies is disclosed. The system may include a computingdevice configured to allow user-creation of an arrangement of objectswhere each of the objects represents a semantic object and thearrangement represents an analysis to be performed as part ofimplementation of a policy. The system may also include a data store forstoring data and a policy engine configured to perform analysis createdwith the computing device with respect to the data.

In an embodiment, the computing device is further configured to convertthe arrangement of objects to a form suitable for use by the policyengine. The arrangement of objects may include a first objectrepresentative of a first semantic object and a second objectrepresentative of a second semantic object where the second semanticobject is an attribute of the first semantic object. In an embodiment,the computing device is further configured to provide user-definedcriteria for said semantic objects and may allow user selection of adata analysis technique to be applied during implementation of thepolicy. Further, the computing device may be configured to include ananalytic object as part of the arrangement, the analytic objectrepresentative of a particular analysis to be performed by the policyengine when applying the policy.

In yet another embodiment, a computer-readable storage medium havingstored thereon instructions for controlling at least one processor ofone or more computer systems to generate executable instructions isdisclosed. The instructions, in an embodiment, include instructions thatcause said at least one processor to provide a plurality of objects to auser through an interface, the objects representative of semanticobjects; instructions that cause said at least one processor to receivean arrangement of a subset of said objects, the arrangementrepresentative of an analysis to be performed as part of implementationof a policy, said arrangement based at least in part on interaction bythe user with the interface; and instructions that cause said at leastone processor to generate, based at least in part on the arrangement,executable instructions suitable for implementation by an applicationconfigured to operate according to the instructions.

In an embodiment, one of said objects includes a field configured toallow user-definition of criteria for a semantic object corresponding tothe object. The arrangement may include a first object representative ofa first semantic object and a second object representative of a secondsemantic object, the second semantic object being an attribute of thefirst semantic object. In addition, in an embodiment, the arrangement ofthe subset of said objects includes an analysis object representative ofa data analysis technique to be applied during implementation of thepolicy. The instructions may further comprise instructions that causesaid at least one processor to provide an analytic object representativeof a data analysis technique to be performed as part of implementing thepolicy; the arrangement may be based at least in part on an associationof the analytic representation with at least a subset of the objects.The instructions may also comprise instructions that cause said at leastone processor to provide an analytic object representative of ananalysis to be performed as part of implementing the policy, where thearrangement is based at least in part on an association of the analyticrepresentation with at least a subset of the objects. In an embodiment,the arrangement includes a first object connected with a second object.Also, the instructions may include instructions that cause said at leastone processor to receive a grouping of a plurality of members of thesubset of objects and performing a user-specified analysis on datacorresponding to the members.

For a fuller understanding of the nature and advantages of the presentinvention, reference should be made to the ensuing detailed descriptionand accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a simplified block diagram of a computer system that may beused to practice an embodiment of the present invention;

FIG. 2 shows an example of an environment in which embodiments of thepresent invention may be practiced;

FIG. 3 shows a data set of an ontology and a matrix encoding the dataset, in accordance with an embodiment;

FIG. 4 shows the data set and the matrix of FIG. 3 in partitioned form;

FIG. 5 shows results of a map function derived from the matrix of FIG.4, in accordance with an embodiment;

FIG. 6 shows an inference library created from the map function outputof FIG. 5;

FIG. 7 shows a method for processing data according to the processdemonstrated in FIGS. 3-5, in accordance with an embodiment;

FIG. 8 shows a graphical representation for updating an inferencelibrary, in accordance with an embodiment;

FIG. 9 shows a diagrammatic representation of a logical architecture ofa semantic data store, in accordance with an embodiment;

FIG. 10 shows a diagrammatic representation of mappings betweenontological and relational meta-models that may be used with thearchitecture of FIG. 10, in accordance with an embodiment;

FIG. 11 shows a diagrammatic representation of a semantic data store andthe relationships between the semantic data store, an ontology, and anontological meta-model, where the semantic data store may be createdusing the mappings of FIG. 11;

FIG. 12 shows a diagrammatic representation of a modular reasoningsystem in accordance with an embodiment;

FIG. 13 shows a method for modularly reasoning data in accordance withan embodiment;

FIG. 14 shows a method for modularly reasoning data in accordance withanother embodiment;

FIG. 15 shows an example of a graphical representation of a policy, inaccordance with an embodiment;

FIG. 16 shows an example of another graphical representation of apolicy, in accordance with an embodiment;

FIG. 17 shows an example of yet another graphical representation of apolicy, in accordance with an embodiment;

FIG. 18 shows a method for creating policies, in accordance with anembodiment; and

FIGS. 19A-19E show an example of various pages of an interface that maybe used in accordance with an embodiment.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, for the purposes of explanation, specificdetails are set forth in order to provide a thorough understanding ofembodiments of the invention. However, it will be apparent that theinvention may be practiced without these specific details.

The following description describes an embodiment of the presentinvention in the business policy domain, and specifically withimplementing business policies using ontologies that encode businessdata. However, the scope of the present invention is not restricted tobusiness policies, but may be applied to other domains or applications.For example, any domain or application where a set of rules or criteriais used to analyze data may make use of the present invention. Examplesof domains in which embodiments of the present invention may be usedinclude segregation of duties, separation of powers, transactionmonitoring, fraud or other crime detection, semantic web applications,and generally applications dealing with large sets of data.

In general, embodiments of the present invention provide techniques forcreating policies to be applied to data. As used herein, unlessotherwise clear from context, a policy is a set of one or moreconditions and a set of one or more actions to be taken when the set ofconditions is met. For example, a policy may be that all transactions ofa certain type (such as credit card charges) over a specified amountrequire approval by a person of a specified class, such as a manager. Inthis example, the conditions of the policy are that transactions have aspecified type and amount and an action of the policy is authorizationof transactions meeting the conditions by a person of a specified class.An action of a policy may also be simply identification of data thatmeet the policy's condition(s). For example, a policy may specify thatall transactions of a certain type and over a certain amount should beidentified. In this example, the conditions are the same as in theprevious example, but the action is identification of transactionsmeeting the conditions so that, for example, a manager may review theidentified transactions and investigate any transactions he or she deemssuspicious.

Typically a policy is used to implement a business policy which is oneor more rules, guidelines, and/or principles related to the conduct of abusiness. For instance, a business policy specifying that invoices overa specific amount require manager approval may be implemented bycreating a policy that includes criteria for identifying invoices overthe specified dollar amount from information stored in one or more datastores.

In a specific embodiment, business data is encoded in an ontology andthe ontology is processed in order to ensure that business policies arefollowed. Processing the ontology involves applying graph partitioningtechniques in order to distribute the data over a plurality of reasonerinstances, where a reasoner instance is one or more processorsimplementing one or more reasoners. Typically, each reasoner instancewill comprise a single processor implementing a single reasoner,although more processors and/or reasoners may be possible in a reasonerinstance. MapReduce techniques, discussed below, may be used tocoordinate the actions of a plurality of reasoners operating over thenodes. Algorithmic matrix-based methodology is used throughout thepartitioning and reasoning process.

Turning now to the drawings, FIG. 1 is a simplified block diagram of acomputer system 100 that may be used to practice an embodiment of thepresent invention. Computer system 100 may serve as a user workstationor server, such as those described in connection with FIG. 2 below. Asshown in FIG. 1, computer system 100 includes a processor 102 thatcommunicates with a number of peripheral subsystems via a bus subsystem104. These peripheral subsystems may include a storage subsystem 106,comprising a memory subsystem 108 and a file storage subsystem 110, userinterface input devices 112, user interface output devices 114, and anetwork interface subsystem 116.

Bus subsystem 104 provides a mechanism for letting the variouscomponents and subsystems of computer system 100 communicate with eachother as intended. Although bus subsystem 104 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple busses.

Network interface subsystem 116 provides an interface to other computersystems, networks, and portals. Network interface subsystem 116 servesas an interface for receiving data from and transmitting data to othersystems from computer system 100.

User interface input devices 112 may include a keyboard, pointingdevices such as a mouse, trackball, touchpad, or graphics tablet, ascanner, a barcode scanner, a touch screen incorporated into thedisplay, audio input devices such as voice recognition systems,microphones, and other types of input devices. In general, use of theterm “input device” is intended to include all possible types of devicesand mechanisms for inputting information to computer system 100. A usermay use an input device in order to execute commands in connection withimplementation of specific embodiments of the present invention, such asto implement, define policies, and/or configure various components of anenterprise system, such as that described below in connection with FIG.2.

User interface output devices 114 may include a display subsystem, aprinter, a fax machine, or non-visual displays such as audio outputdevices, etc. The display subsystem may be a cathode ray tube (CRT), aflat-panel device such as a liquid crystal display (LCD), or aprojection device. In general, use of the term “output device” isintended to include all possible types of devices and mechanisms foroutputting information from computer system 100. Results of implementingpolicies, defining policies, and configuring various components of acomputer system may be output to the user via an output device.

Storage subsystem 106 provides a computer-readable medium for storingthe basic programming and data constructs that provide the functionalityof the present invention. Software (programs, code modules,instructions) that when executed by a processor provide thefunctionality of the present invention may be stored in storagesubsystem 106. These software modules or instructions may be executed byprocessor(s) 102. Storage subsystem 106 may also provide a repositoryfor storing data used in accordance with the present invention, forexample, the data stored in the diagnostic data repository. For example,storage subsystem 106 provides a storage medium for persisting one ormore ontologies. Storage subsystem 106 may comprise memory subsystem 108and file/disk storage subsystem 110.

Memory subsystem 108 may include a number of memories including a mainrandom access memory (RAM) 118 for storage of instructions and dataduring program execution and a read only memory (ROM) 120 in which fixedinstructions are stored. File storage subsystem 110 provides persistent(non-volatile) storage for program and data files, and may include ahard disk drive, a floppy disk drive along with associated removablemedia, a Compact Disk Read Only Memory (CD-ROM) drive, an optical drive,removable media cartridges, and other like storage media.

Computer system 100 can be of various types including a personalcomputer, a portable computer, a workstation, a network computer, amainframe, a kiosk, personal digital assistant (PDA), cellulartelephone, a server, or any other data processing system. Due to theever-changing nature of computers and networks, the description ofcomputer system 100 depicted in FIG. 1 is intended only as a specificexample for purposes of illustrating the preferred embodiment of thecomputer system. Many other configurations having more or fewercomponents than the system depicted in FIG. 1 are possible.

FIG. 2 shows a simplified block diagram of an enterprise computer system200 that may be used to practice an embodiment of the present invention.It should be understood that, generally, enterprise computer systemsvary greatly and, as a result, specific embodiments may include more orless components than shown in the figure and that the specificcomponents shown in FIG. 2 are only intended to provide an example forthe purposes of illustration.

In accordance with an embodiment, the enterprise computer system 200includes a first location 202 and a second location 204 communicativelyconnected by a network 206, such as the Internet or any suitablecommunications network or combination of networks. In an embodiment, thefirst location 202 and second location 204 correspond to separatephysical locations of a business, such as offices in two separatecities, states, or countries. While FIG. 2 shows two locations, itshould be understood that a business may have only a single location andmay include more than two locations. As shown in the drawing, theenterprise computer system 200 may include one or more user workstations208, a development server 210, and a developer workstation 212. The userworkstation 208, development server 210, and/or development workstation212 may be physically present at any of the locations, or at separatelocations. In an embodiment, the user workstation 208 and developmentserver 210 are communicatively connected to the network 206 so as toaccess various components of the enterprise computer system. Forexample, the user workstation 208 may include a browser used for viewingcontent provided from the Internet and/or from other systems within thebusiness. Further, the developer workstation 212 may be connected to thenetwork 206 through the development server 210 and may be adapted toenable certain employees within the organization to configure, install,modify, and perform other actions in connection with the business'computing systems. As an example, a developer within the organizationmay utilize the developer workstation in order to create policies thatare used to define policies and execute one or more applications thatstores data in one or more ontologies, and that reason the dataaccording to the policies in accordance with various embodiments of theinvention. Instructions for controlling the applications and the definedpolicies may be sent over the network 206 to an appropriate computingdevice executing the one or more applications.

As noted above, the first location 202 may include various computersystems used in operating the business. For example, as depicted in FIG.2, the first location 202 includes a web server 214 configured toreceive requests from various users, such as from a user of the userworkstation 208, and to respond to the requests over the network 206.While FIG. 2 shows the web server as a hardware component, as with anyof the servers described herein, the web server may also be a softwaremodule operating on a computer system. Responses from the web server 214may be provided from the web server 214 itself or through the web server214 but from a variety of sources in communication with the web server214, such as from components of an internal computer system of the firstlocation 202 or from other web servers located at other, possiblythird-party, locations.

In an embodiment, the web server 214 is communicably coupled to anapplication server 216, which is a hardware component or software moduleconfigured to run one or more applications, such as one or more policyengines and other applications for managing organizational data. As isknown, a user of the user workstation 208 may send a request to the webserver 214 that specifies a specific action to be taken in connectionwith an internal business application implemented on the applicationserver 216. The web server 214 then relays the request to theapplication server 216 which takes the specified action and returns theresult of that action to the web server 214, which in turn relays theresult to the user workstation 208. In accordance with an embodiment,the web server 214, or other component, may modify the content returnedto the user workstation 208 in accordance with one or more policiesapplicable to a user of the user workstation 208.

As shown in the example of FIG. 2, the application server 216 interactswith data stored in a first data store 218 and a second data store 220,each of which may store data relevant to the business' operation, suchas in one or more relational or other databases. While the disclosedexample shows the first location 202 having two data stores, it shouldbe understood that the first location 202 may have less than two datastores or more than two data stores. Information in the data stores caninclude a wide variety of data, such as data relating to businesstransactions, invoices, human resources data, user account data,receipts, bank account data, accounting data, payroll data, andgenerally, any data relevant to the operation of a particular business.Information from the data stores 218, 220, and other sources, may beextracted from the data stores, converted to a uniform format, andstored in an ontology in accordance with an embodiment.

In an embodiment, the second location includes its own web server 222,application server 224, first data store 226, and second data store 224which may be configured to function similarly to the identically namedcomponents above.

FIG. 7 shows a flowchart demonstrating a method 700 for processing datain accordance with an embodiment. The method 700 (or any methoddisclosed herein), for example, can include techniques described belowin connection with FIGS. 3-8. As with any method disclosed herein, themethod depicted in FIG. 7, or variations thereof and/or combinationsthereof, may be implemented by software (e.g., code, instructions,program) executing on a processor, by hardware, or combinations thereof.The software may be stored on a computer-readable storage medium, forexample, in the form of a computer program comprising a plurality ofinstructions executable by one or more processors.

In an embodiment, data is stored in an ontology by creating ontologydata from various business data sources at a data storage step 702. Asnoted below, the data can be enterprise business data or, generally, anytype of data. Storage of the data can be performed in a variety of ways.For instance, in an embodiment, a batch process is periodically executedthat causes data stored in data stores to be compiled into an ontology.For instance, data stored in a first form can be transformed using oneor more adapters configured to convert data from a first form to a formsuitable for storage in the ontology. In addition, automatic Extract,Transfer, and Load (ETL) operations from a business' data sources to asemantic data store that embodies the ontology may be defined and set torun when trigger conditions are met, such as at certain times or when acertain amount of data has been changed.

At a partitioning step 704, the ontology data is partitioned so as to bedistributable among a plurality of processors. Each processor mayimplement the same or a different reasoner instance. Partitioning thedata may include encoding the ontology data in a matrix, such as in amanner described below, and partitioning the matrix using one or morematrix partitioning techniques. In alternate embodiments, the ontologydata is not necessarily encoded in a matrix, but is distributed usingother methods. For instance, because ontologies can be represented asgraphs, such as directed graphs, graph partitioning techniques may beused. Generally, any technique for partitioning data among a pluralityof reasoners may be used.

At a distribution step 706, the partitioned ontology data is distributedamong a plurality of processors, each of which may implement ofinstances of the same or a different reasoner. Techniques, such as thosedescribed in Map Reduce: Simplified Data Processing on Large Clusters,by Jeffery Dean and Janj ay Ghemawat, published at the Sixth Symposiumon Operating System Design and Implementation, which is incorporated byreference for all purposes, may be used to coordinate the actions of thereasoners. In this manner, the processing of the ontology data isperformed by a plurality of reasoners so as to reduce the time necessaryfor processing. At a combination step 708, the results of the processingby the plurality of reasoners are combined into a set of processed data.Combination may include connecting results of separate processingaccording to relationships associating different sets ofseparately-processed data, such as data encoded in intersection vectors,such as those described above. Again, in an embodiment, MapReducetechniques may be used to coordinate combination the results from thereasoners.

In this manner, the work done in processing an ontology is performedefficiently and more quickly than if the ontology was processed with asingle reasoner. Other benefits in using the above method are alsoincurred. For example, the embodiments of the disclosed method allow forefficient handling of new and/or modified data, as described in moredetail below in connection with FIG. 8.

As businesses and other organizations operate, the data they storechanges as a result of business operations. New invoices are created,new payments are made to vendors, employee roles change, new people ororganizations become customers, peoples' position within an organizationchanges, and other events happen during the course of operating abusiness that may influence the addition, subtraction, or modificationof associated data. Moreover, because the amount of data stored by abusiness is typically very large, creation or modification of anontology based on the data typically takes a large amount of resourcesand, therefore, is performed as a batch process, often during times whena business' systems are under a lighter work load, such as at a time ofthe day when many employees may be at home or when most potentialcustomers are asleep.

FIG. 3 shows a representation of an example data set 300 stored in anontology, in accordance with an embodiment. It should be understood thatthe data set 300 is given for purposes of illustration and typicallydata sets for businesses or other organizations will be larger and morecomplex. In an embodiment, the data set 300 comprises a plurality oftriples 302 where a triple includes a first node, a second node, and arelationship between the first and second nodes. For example, a firsttriple 304 includes a first node A, a second node B, and a relationshipPi between A and B. Each node in a triple may represent a data point,such as a piece of information stored in a database of a business orused in connection with operation of such a database. Each node can be asimple piece of information such as an employee name, a particular lineitem, a particular invoice, a class of employees, or generally any typeof information or class of information that can be stored. Each node canalso represent more complicated sets of information such as completedata records or files or classes of information including attributes ofthe class.

As a concrete example, A may represent John Doe and B may represent aspecific class of employee, such as a manager. In the relationship shownin the example of the first triple 304, Pi connecting A to B indicatesthat John Doe is a manager. As shown in the example in FIG. 3,relationships between nodes may be directional, as indicated in theexample by an arrow. For example, continuing with the example of thefirst triple 304, the arrow extending from A to B indicates that JohnDoe is a manager but not necessarily that all managers are John Doe. Anode may relate to more than one other nodes. For example, FIG. 3 showsa second triple 306, showing a relationship P₃ between the node A and anode D. Thus, considering both the first triple 304 and the secondtriple 306, it can be seen that the node A relates to both B and to D bytwo different relationships. Specifically, A is related to B by P₁ andrelated to D by P₃. For example, D may indicate a class of employeeshaving access to a particular system, such as a security system,accounting computer system, and the like. Thus, read together, the firsttriple 304 and second triple 306 indicate that John Doe is a manager,and John Doe also has access to the system represented by D. Inaddition, various nodes can be related to each other through inferredrelationships. Briefly, for example, FIG. 3 shows a third triple 308showing a relationship P₄ between nodes B and C. Continuing the examplediscussed above, C may be a specific set of accounting data for anorganization. Thus, the relationship represented by the third tripleindicates that all managers have access to the accounting data. Thus,when reading the first triple 304 and the third triple 308 together, arelationship between A and C may be inferred that John Doe has access toaccounting data because John Doe is a manager. Further details oninferred relationships are provided below.

In an embodiment, the data set 300 may be represented in a matrix. Forexample, FIG. 3 shows a matrix 310 in accordance with an embodiment. Thematrix 310, in the example shown, is formed by a series of row vectors,each row vector corresponding to a relationship of the data set 300. Asshown in the example, the order of the row vectors of the matrix 310does not have any particular significance; however, specific orderings,such as an ordering proceeding according to an index of possiblerelationships, and other orderings may be used.

Each column vector in the matrix 310 represents a node and, as with therow vectors, the columns need not be in any particular order, but maybe. Matrix 310 comprises an entry at each intersection of a row vectorand a column vector. The entries in the matrix 310 store values thatencode data set 300. In an embodiment that values for the entries inmatrix 310 are either zeros or ones. Although the example given showsentries having values of 0 or 1, other values, such as Boolean values of“true” and “false,” or generally any set of distinguishable values mayalso be used in alternative embodiments.

As noted, the columns and rows of the matrix 310 may or may not be inany particular order. For instance, in an embodiment, data is extractedfrom one or more data stores and used to construct the matrix and themanner or order in which the matrix is constructed or extracted maydictate the matrix's initial form. For instance, in an embodiment, rowsmay be added to the matrix sequentially as relationships betweenextracted data are determined. In another embodiment, columns may beappended to the matrix as each data point is examined to determine therelationships associated with the data point.

In an embodiment, a particular row includes entries of zero or one. Therelationship associated with the row may be determined by the oneentries. Specifically, a column of the matrix that intersects the row ata one entry is associated with a node involved in the relationship.Likewise, a column of the matrix that intersects the row at a zero entryis associated with a node that is not involved in the relationship.Thus, counting from the top, looking at the first row of the matrix 310which corresponds to the relationship P₁₂, the intersection between theA column and the P₁₂ row includes a zero entry thereby indicating thatrelationship P₁₂ does not involve the node A. The intersections of theP₁₂ row with columns J and I includes entries of one, indicating thatthe relationship P₁₂ involves I and J. In a like manner, ones or zeroesare filled in matrix 310 to represent the relationships represented bydata set 300.

It should be understood that, while FIG. 3 shows a matrix representationof the data set 300, other representations can be used. For example,matrices may be constructed differently than shown in the figures. Forinstance, in an alternative embodiment, row vectors may correspond tonodes while column vectors may correspond to relationships. As anotherexample, as is known, data sets stored in ontologies may be representedin a graph, a directed graph, or another representation which may encodedata differently. Techniques analogous to the techniques describedbelow, such as techniques for graph partitioning, may also be used inaccordance with the present invention. Further, it should be understoodthat while the examples in the figures show graphical representations ofspecific matrices, including the entries of the matrices, matricescorresponding to data sets will typically be too large to be displayedin the same manner, but may be stored in computer memory (eithervolatile or non-volatile) in a manner dictated by a specific applicationused to create the matrices or other representations of the data.

In an embodiment, the matrix 310 is partitioned into a convenient form,for example, by using known techniques of linear algebra. For instance,the matrix 310 may be placed into block form by using elementary rowoperations such as swapping rows. Column operations, such as switchingcolumns, may also be used. When row, column, or other operations areused, an index vector, list, or other mechanism that may be part of thematrix or stored in another location, may be updated to keep track ofwhich relationships and/or nodes correspond to each vector. For example,each entry of the first row may include information (such as a string ornumber) identifying a particular relationship and the first entry ofeach column may include information identifying a particular node. Inthis manner, when a row or column operation is performed, theidentifying information of the associated rows and/or columns areaffected by the operation in a way that keeps track of the rows and/orcolumns. As a concrete example, if the first and second rows areswitched, in an embodiment, the information identifying the first rowmoves to the second row and the information identifying the second rowmoves to the first row.

In an embodiment, partitioning a matrix includes arranging the columnssuch that the matrix encodes the directions of the relationships of therepresented triples. Thus, the columns may be arranged such that thecolumn corresponding to the first node in a triple is to the left of thecolumn corresponding to the second node in the triple. Otherconfigurations of matrices that encode the direction of therelationships may also be used, such as the inclusion of an additionalencoding column that includes entries that correspond to the directionof triples included in a particular row. For instance, an additionalcolumn may be added to the matrix 310 so that the intersection of a rowwith the additional column includes a 0 if the order of the columnscorresponds to the direction of the relationship encoded in the row anda 1 otherwise. For instance, the first row has a 1 in the intersectionswith the I and J columns, but the J column appears before the I column,so the order to the I and J columns does not correspond to therelationship P₁₂ extending between the I and J nodes. Therefore, in thisexample, an encoding column would have a 1 in the intersection of thefirst row with the encoding column to indicate that the relationship P₁₂extends from I to J.

In an embodiment, with the columns arranged, the rows are arranged sothat the matrix is in block form. Matrices used in accordance with thepresent invention will generally be sparse matrices because each row, inan embodiment, will have only two non-zero entries corresponding to thespecific data represented in the row. As a result, such partitioning maybe performed to form a matrix having more than one block which isconvenient for visualizing and processing of the data set 300, asdescribed more fully below.

Generally, when a matrix is used to encode data, the matrix can bepartitioned into a convenient form, such as block form, using varioustechniques. For example, spectral partitioning can be used to partitionincidence, Laplacian, or other matrices that encode a graphrepresentative of ontological data. Likewise, multilevel coarsening andpartitioning techniques, such as those that coarsen, partition, and thenuncoarsen a matrix may be used. Of course, hybrid approaches of theabove techniques and/or other techniques can be used as well.

It should be noted that such rearrangement of the columns may not bestraight forward if a data set includes a circuit, which is a set of oneor more nodes and one or more relationships arranged such that aninferred or direct relationship exists between a node and itself. Forexample, a circuit exists in a situation where A relates to B, B relatesto C, and C relates to A, with the directions of the relationshipsextending from A to B, from B to C, and from C to A. With a circuit, itis not straight forward to order the columns in order to encode thedirections of the relationships without taking additional measures. Forinstance, in the circuit described above, the C column would have tooccur simultaneously before and after the A column. Nevertheless, onewith ordinary skill in the art would recognize that such situations maybe remedied through a variety of techniques. For example, a data set maybe pre-processed to locate any circuits. If any circuits are found,triples may be removed from the data set to break any circuitous paths.For instance, the triple of C to A may be removed in the example givenabove so that A does not indirectly refer to itself. The removed triplesmay be separately processed and the results of the separate processingmay be combined with results of processing the modified data set.

Because the data set 300 is stored in an ontology, it can be consideredas a graph, having vertices being the nodes and the relationships beingedges. In an embodiment, partitioning a matrix representative of a dataset can be visualized by equivalent operations on a graph representingthe data set. For instance, FIG. 4 shows a representation of a graph 400of a data set such as the data set 300, above, which shows thetransitive properties of the data set. For example, if A is related to Band B is related to C, then the graph shows edges connecting B to both Aand C. As shown in the example, the graph 400 includes a first subgraph402 and a second subgraph 404. The first subgraph 402 and the secondsubgraph 404 are related to each other through the relationship P₇connecting node E, which is in the first subgraph 402, to node F, whichis in the second subgraph 404. In this manner, processing the data set300 can be performed by separately processing data in the subgraphs andcombining the results. For example, the data in the first subgraph 402may be processed in a first processor executing instructions for a firstreasoner instance, the data in the second subgraph 404 may be processedin a second processor executing instructions for another reasonerinstance, which may employ the same or a different set of rules forprocessing than the first reasoner instance. Either the first or second(or another) processer may be used to combine the results according tothe relationship P₇.

It should be understood that data sets will vary and, as a result,decomposition of a graph representing a data set will vary accordingly.For instance, a graph may be partitioned into subgraphs that aredisconnected, or may be partitioned into subgraphs that are connected toone another by more than one edge. In addition, a typical data set, inaccordance with an embodiment, will be partitioned into more than twosubgraphs which may be processed separately. Further, data in somesubgraphs may be processed in one processor, while data in othersubgraphs may be processed in another processer or processors.

Turning to the matrix representation, FIG. 4 also shows a matrix 412which has been partitioned into a convenient form. For example, thematrix 412 in an embodiment is a matrix resulting from thetransformation of the matrix 310, described above. As described above,the columns of the unpartitioned matrix 310 have been rearranged suchthat they encode the direction of the relationships between the nodes.In an embodiment, if a relationship extends from a first node to asecond node, then the column associated with the first node is placedbefore the column associated with the second node. For example, becausethe relationship P₁ extends between A and B, the A column is placedbefore the B column.

Further, the rows of the matrix 412 have been arranged so as to put thematrix in block form which, as described below, results in partitioningthe data into separately processable partitions. As discussed above,many different techniques for partitioning matrices into block form maybe used in accordance with various embodiments. As shown, the matrix 412includes a first vector set 408 (SET A) comprising the upper seven rowsof vectors and a second vector set 410 (SET B) comprising the lower sixrows of vectors, where the first vector set 408 is above the secondvector set 410. An intersection vector set 412 comprises the row vectorsthat are common to both the first vector set 408 and second vector set410. As discussed, the matrix and sub matrices of FIG. 4 are providedfor the purposes of illustration and, generally, matrices used inaccordance with various embodiments may have vector sets and submatrices having different characteristics, such as more or less rows.

As shown in the example, the first vector set 408 includes a firstsubmatrix 414 in the upper left corner that comprises entries that areeither zero or one and a first zero matrix 416 in the upper left cornerthat comprises entries that are all zero. In an embodiment, the firstsubmatrix 414 is situated to the left of the first zero matrix 416.Likewise, the second vector set 410 includes a second submatrix 418 anda second zero matrix 420 where the second submatrix 418 sits to theright of the second zero matrix 420 and the second submatrix 418includes entries being zero or one and the second zero matrix 420 havingentries all zero. In this manner, it can be seen that the partitionedmatrix 406 is partitioned into discreet blocks and may include a vectorconnecting the blocks. While the partitioned matrix 406 is composed offour block matrices and the intersection vector 412, it should beunderstood that data sets, in general, in accordance with an embodiment,will be partitioned into a larger or smaller number of blocks which mayor may not be separated by non-zero intersection row vectors. Inaddition, it should be understood that the particular positioning of theblocks of the matrix 406 is made according to mathematical conventionwith the blocks located along a main diagonal of the matrix 406, butthat other configurations are possible.

Returning to the example in the drawing, the first submatrix 414 encodesthe first subgraph 402 while the second submatrix 418 encodes the secondsubgraph 404 in the manner described above. The intersection vectorencodes the relationship between the first subgraph 402 and the secondsubgraph 404. If a graph of a data set includes two disconnectedsubgraphs, a partitioned matrix representation may not include anyintersection vectors between blocks representing the disconnectedsubgraphs. In addition, one or more row vectors of all zero entries maybe situated between blocks representing disconnected subgraphs.

In an embodiment, a map function and a reduce function are employed inorder to distribute the reasoning of an ontology among variousprocessors and to combine the results of the distributed reasoning.Reasoning an ontology may include application of a predefined set ofrules to the data of the ontology. As an example, a commonly used rulein reasoning ontologies is the transitive rule where, if node A relatesto node B and node B relates to node C, then node A relates to node C.Other rules, depending on specific applications, may be used in additionto or in place of the transitive rule. In an embodiment, the mapfunction takes as input data corresponding to a subgraph of a graphrepresenting an ontology and a set of rules to be used by a reasoner toprocess the individual triples represented in the subgraph. For asubgraph and set of rules input to the map function, the output of themap function includes data corresponding to a subgraph (typically adifferent subgraph) and an inferred vector which may encode informationabout one or more triples. In an embodiment, the subgraphs output by themap function may include nodes that are common to more than one subgraphso as to encode any relationships between subgraphs.

FIG. 5 shows a specific example of matrices resulting from a mapfunction that can be used in accordance with an embodiment.Specifically, FIG. 5 shows a first vector set 500 and a second vectorset 502 in accordance with an embodiment. In an embodiment, the firstvector set 500 encodes the first vector set 408 and intersection vector412 described above in connection with FIG. 4. Referring back to FIG. 4,the first vector set 500 encodes the first subgraph 402 and the tripleincluding nodes E and F, and the relationship P₇ extending between E andF. In this manner, the first vector set 500 encodes the first subgraph402 and the triple connecting the first subgraph 402 to the secondsubgraph 404. In an embodiment, the first vector set 500 encodes thefirst subgraph 402 and triple including E, F, and P₇ in a manner similarto that described in connection with FIGS. 3 and 4.

Similarly, the second vector set 502 encodes the second vector set 410and the intersection vector 412 described above in connection with FIG.4, thereby encoding the second subgraph and the triple represented by E,F, and P₇. As shown in FIG. 5, in an embodiment, the row vectors of thefirst set of vectors 500 are simply the row vectors of the first dataset 408 and intersection vector 412. In this manner, the map functionoutputs the first vector set 500 and second vector set 502. Subgraphscorresponding to the first vector set 500 and second vector set 502 maybe ascertained from the entries in the vector sets, as described above.In an embodiment, the first vector set 500 forms a first matrix 504which may be in block form and whose columns and rows represent nodesand relationships, respectively, as described above.

As noted above, the map function also outputs inferred vectors which mayencode the relationship between two or more nodes as determined by areasoner. For example, a set of rules may include a transitive rule foran ontology which provides, for example, that if A is related to B and Bis related to C then A is related to C. The set of rules may alsoinclude information identifying which rows should be considered whenimplementing the transitive rule. The transitive rule in processing ofontologies is convenient because when matrix representations are used,as described above, processing the transitive rule on a subgraph can beperformed using an OR operation of the relevant rows which iscomputationally efficient. In an embodiment, an OR operation on aplurality of rows is performed by performing an OR operation oncorresponding entries in the rows. For example, if the third entry ofone row is a zero and the third entry of another row is zero, an ORoperation performed on the two rows will have a zero in the third entry.If the third entry of both rows is a one, then an OR operation performedon the two rows will have a zero in the third entry. If one of the rowshas a one in the third entry and the other row has a zero in the thirdentry, then the result of an OR operation performed on the two rows willhave a one in the third entry.

In an embodiment, the inferred vectors form a set of inferred vectorswhose columns and rows encode triples as described above. For example, afirst inferred vector set 506 results from processing the first vectorset 500 according to a plurality of user-selected or predefined rules ofa reasoner. Likewise, a second inferred vector set 510 results fromprocessing the second vector set 502. In the example shown, the firstrow of the first inferred vector set 506 is a result of performing an ORoperation on the rows P₁, P₄, P₆ and P₇ of the first submatrix 504. Thisparticular operation, for instance, may be chosen by a user of thereasoner and any suitable operation or operations may be used. Likewise,the remaining rows of the inferred vector set 506 are formed usingvarious OR operations on various rows of the first submatrix 504depending on the particular rules chosen by the user. Generally, thetype of operations used to make inferred vector sets will vary dependingon specific applications and reasoners and it should be understood thatthe particular operations used to form the inferred vector sets arechosen merely as an example.

In an embodiment, a reduce function is constructed or provided whoseinput includes information about subgraphs and inferred triples fromeach subgraph. For example, the input of the reduce function may includea list of nodes directly related to nodes of the subgraph. Thus, theinput of the reduce function may include all the nodes of the subgraphas well as one or more nodes of another subgraph related to the inputsubgraph by a relationship. For example, in reference to the firstsubgraph 402 and second subgraph 404 shown in FIG. 4, the input of thereduce function may include the list {{A, B, C, D, E, F}, {F, G, H, I,J}}. In this example, the nodes A, B, C, D, E, and F are from the firstsubgraph 402 and the nodes F, G, H, I, and J are from the secondsubgraph 404. The node F is included in both lists because it is thenode in the second subgraph 404 to which the first subgraph 402 refersthrough the relationship P₇. The input of the reduce function may alsoinclude a list of inferred triples within the subgraphs, such as atriple including nodes A and E.

The reduce function determines, based upon the input, whether additionalreasoning should take place. For instance, referring to the sameexample, because the first subgraph 402 and second subgraph 404 arerelated to each other by the relationship P₇, the reduce function thentakes the inferred triples from each subgraph and applies the rules ofthe reasoner to the inferred triples input to the function and returns alist of inferences. For complicated data sets, the reduce function maybe applied repeatedly or recursively to ensure that desirable inferencesare identified. Thus, for example, the output of the reduce function mayinclude an inferred triple that includes the nodes A (from the firstsubgraph 402) and J (from the second subgraph 404), because A and J areindirectly related to one another through a series of relationships.

FIG. 6 shows an example of the output of a reduce function using theinput from the example shown in FIG. 5. In particular, FIG. 6 shows amatrix 600 (inference library) whose columns correspond to nodes andwhose rows correspond to inferred triples. The inferred triples may be,for example, a result of applying a reasoner to the first inferredvector set 506 and second inferred vector set 510 of FIG. 5. As aspecific example, the first row of the matrix 600 shows an inferencefrom A to J because there is a 1 in the intersection of the A and Jcolumns with the first row. The first row also shows the nodes involvedin an inference from A to J. For instance, the intersection of the B, C,E, F, G, I columns with the first row includes a 1 while theintersection of the D and H columns includes a 0. As a result, thematrix 600 encodes information indicating that an inference from A to Jmay include a path extending through nodes B, C, E, F, G, and I.Likewise, the fourth row of matrix 600 encodes another inference from Ato J that may include a path extending through nodes C, D, E, F, G, andI.

FIG. 8 demonstrates how embodiments of the disclosed invention allow forefficient handling of new, deleted, and/or modified data. In anembodiment, an ontology 802 representing a data set is partitioned intoa plurality of subontologies, represented in the figure by SO₁ throughSO_(n). When a piece of data of the data set is modified, one or moretriples of an ontology representing the data set change when theontology is updated. For instance, FIG. 8 shows a vector 804representative of a triple changed because of a change in acorresponding data point. Because of the partitioning of the ontology inaccordance with embodiments of the present invention, it is possible tomap the changed vector to only those subontologies affected by thechange. For example, the changed vector may be mapped to a singlesubontology, a subset of the subontologies, or all subontologies.Because the vector is mapped to only the affected subontology orsubontologies, updating the ontology may involve only updating theaffected ontologies. An inference library 806, such as the inferencelibrary represented by the matrix 600. described above in connectionwith FIG. 6, may be updated, for example by using MapReduce techniques,by updating only the portions of the reference library affected by achange in affected ontologies instead of updating the complete referencelibrary. Further, when multiple vectors representing triples change, thevectors can be likewise mapped to affected subontologies and updatingthe entire ontology can then be spread over a plurality of processors.Thus, the amount of resources spent updating an ontology based onchanged data may be significantly reduced.

FIG. 9 shows a general representation of an architecture for extractingsemantic data from various sources in accordance with an embodiment. Thearchitecture 900, in an embodiment, includes a semantic data store 902which is modeled by a business ontology 904 according to declarativedata mappings, as described in more detail below. Generally, in anembodiment, the business ontology 904 is a representation of semanticconcepts from the semantic data store 902 and the relationships betweenthose concepts. Also, in an embodiment, the semantic data store 902 maybe realized using the W3C OWL-DL ontology language. It should beunderstood that, while the present disclosure refers to various conceptswithin a business domain, the present invention is applicable to anydomain that is associated with one or more organizations that store dataas part of their operations. Therefore, while the following descriptionrefers to a business, the present invention may be applicable to anyorganization.

In the example of FIG. 9, four different data sources of a business areshown, although a business or other entity may use more than four datasources or less than four. Also, the four different data sources may bephysically realized in separate data stores, which may be in separategeographical locations, or two or more data sources may be incorporatedinto a single data store. As shown in the example demonstrated in FIG.9, a business may include a first relational database 906 which ismodeled by a first relational schema 908. As it applies to data storage,a schema is a structure configured to organize the data of one or moredata sources. For example, in a relational database, a schema thatmodels the relational database defines the tables of the database, thefields of each table, and the relationships between the fields andtables.

In the provided example, the business may also include a secondrelational database 910 which is modeled by a second relational schema912. There can be various reasons for having more than one source ofbusiness data, for example for storing data for different aspects of abusinesses' activities, such as sales and human resources. Businessesmay also store data in different forms depending on the particularapplication. For example, in FIG. 9 a light weight directory accessprotocol (LDAP) directory 914 is modeled by a LDAP schema 916. Likewise,a flat file database 918 may be modeled by a flat file schema 920. Thus,in the example shown in FIG. 9, a business may include data from avariety of sources in a variety of formats.

As can be seen in the figure, data from each of the data sources ismapped to the semantic data store 902. In an embodiment, mapping datafrom a data source to the semantic data store 902 is described in moredetail below, but generally includes extracting data from the source andloading it (or a portion of it) into the semantic data store, which mayor may not involve reformatting data from one form to a form suitablefor the semantic data store 902. In addition, mapping data from a datasource to the semantic data store 902 may involve mapping all data fromthe data source or using a filter to only map some data from the datasource. For instance, the data source may include data that is notpertinent to the purposes for which the business ontology 904 is usedand, as a result, only pertinent data would be mapped to the semanticdata store. A filter may be used to control which data is mapped to thesemantic data store. For example, the data mappings above can be used inconnection with Oracle Data Integration (ODI) Tools available fromOracle International Corporation in order to perform ETL processes thatconstrain and filter data from the various data stores and merge thedata into a common format in the semantic data store 902. As describedbelow, once maps are constructed, the maps can be used in automatedprocesses that extract data from one data store and appropriately loadthe data into the semantic data store 902. Extraction and loading ofdata can occur, for example, at predetermined intervals such as once aday, or at predetermined triggers, such as when data is changed.

As shown in the example, data from the first relational database 906 isstored in the semantic data store 902 as well as data from the secondrelational database 910, the LDAP Directory 914, and the flat filedatabase 918. In an embodiment, schemas of various data stores aremapped to the business ontology such that semantic concepts embodied inthe data stores are stored in the business ontology 904. For example,the first relational database 906 may include a plurality of tables,each table having one or more columns. The first relational schema 908may relate tables together in a useful manner, for example, relatingcustomers to invoices such that a customer is related to an invoice forgoods or services purchased by the customer. Thus, relationships definedby the relational schema 908 are mapped to the business ontology 904such that semantic concepts defined by the relational schema 908 arepreserved in the business ontology 904.

FIG. 10 shows an ontological meta-model 1000 which, in an embodiment,provides an implementation for the architecture described in connectionwith FIG. 9 and to which a relational meta-model 1002 is mapped. Theexample given in FIG. 10 may be useful, for example, for mapping datafrom a relational database to the ontological meta-model 1000 althoughit will be understood by those with ordinary skill in the art thatvarious data stores and corresponding data schemas may have differentproperties than what is shown. In an embodiment, the ontologicalmeta-model 1000 comprises a plurality of ontological storage containerswhich includes a container for ontological concepts 1004, which is asuper class of attributes 1006 class, classes 1008 class, and relations1010 class. In other words, attributes 1006, classes 1008, and relations1010 are all types of ontological concepts. In an embodiment, each class1008 is a collection of one or more entities or instances, such asemployees, and each attribute 1006 is a collection of one or moredata-type property of a class, such as a first name. As noted above, theclasses can have super classes. For example a member of an Employeeclass may be a member of a Manager class as well.

Also, in an embodiment, each relation 1010 is a binary relationshipbetween two classes. For example, a relation orgHasEmployees may be arelationship between a member of an organization class and an employeeclass. This relationship, for example, may specify employees that arepart of an organization. Relations 1010 may be further classified interms of their domains (the class or classes from which they relate) andranges (the class or classes to which they relate). Also, in anembodiment, some relations 1010 have super relations. For instanceorgHasEmployees may be a super relation of an orgHasManagers relationbecause, for example, all managers may be employees.

As shown in the diagram, the ontological meta model 1000 also includesstorage for ontological data types 1012, which may be, for example,strings, integers, floats, dates, Boolean, or other types of data. In anembodiment, datatypes are the ranges (value sets) of the attributes andconsist of sets of similar data forms. In the embodiment presented inthe drawings, ontological type data is stored separately from instancedata, which is stored in a hyper-denormalized relationed form. As usedherein, semantic data that is in hyper-denormalized raltioned form isstored such that every attribute is stored in its own table. This formprovides an advantage in that instance data is easily and quicklyaccessible, which in turn allows for a highly distributed approach tosolve problems of inferencing, persistence, and other issues. In otherwords, the architecture in the disclosed embodiment provides the powerand flexibility of ontological storage with the performance of modernrelational database management system. However, one with skill in theart will appreciate that variations are possible and that, in othercontexts, different architecture may be appropriate. For example, onewith skill in the art would recognize that type and instance data may bestored in the same storage system and that instance data need not behyper-denormalized, but that different degrees of denormalization ofdata may be used, and different kinds of instance data may be combinedin one or more containers.

As shown in the drawing, in an embodiment, between the classes arerelations 1010 between the classes 1008 and there may be relations 1010among the relations 1010. Also, each class 1008 is an aggregation ofattributes, in accordance with an embodiment.

As noted above, the relational meta-model 1002 is mapped to theontological meta-model, as described more fully below. In an embodiment,of relational meta-model includes relational concepts 1014 which aresuper classes of tables 1016, columns 1018 and keys 1020. Also, as isknown, each table 1016 is an aggregation of columns. As can be seen,various mappings are provided between various elements of theontological meta-model 1000 in relational meta-model 1002. For instance,in an embodiment, one or more columns of a table are mapped to anattribute of the ontological meta-model 1000. Likewise, tables 1016 aremapped to classes 1008 of the ontological meta-model 1000. As keys 1020define relationships between tables 1016 in the relational meta-model,keys of the relational meta-model 1002 are mapped to relations of theontological meta-model 1010 in a manner preserving the relationshipsbetween the tables 1016. In an embodiment, relational data types 1022are mapped to a ontological data types 1012.

In an embodiment, the relational meta-model may be implemented using arelational database management system (RDBMS) and the meta-data in therelational meta-model is, therefore, readily available. The mappingshown in FIG. 10, in an embodiment, may also be achieved by utilizingApplication Programming Interfaces (APIs) exposed by the systemsimplementing the relational meta-model 1002, such as Java DatabaseConnectivity (JDBC) meta-data, Open Database Connectivity (ODBC)meta-data, and the like.

FIG. 11 shows an example fragment 1100 from a sales ontology data storethat provides an illustrative example how an ontology meta model 1102,sales ontology 1104, and semantic data store 1106 may relate, inaccordance with an embodiment. In an embodiment, the semantic data store1106 is used to store and maintain the instance data that is modeled byone or more ontologies and that has been imported, filtered, and mergedfrom the business data sources, such as those described above inconnection with FIG. 9. In an embodiment, in order to reason overtransactional data with high performance, the semantic data store 1106is formatted as a hyper-denormalized and vertically partitioned system.For example, data derived from n columns of an RDBMS table, in anembodiment, would be stored as n tables in the semantic data store 1106.

In an embodiment, the semantic data store translates policies (queries)expressed in terms of the sales ontology 1104 into queries expressed interms of the semantic store schema, and executing the translated querieson the data store. The actual execution of the query may be delegated toa reasoner. Thus, in an embodiment, a query expressed in terms ofclasses and relations will be translated by the semantic data store 1106in terms of tables and keys. For example, in an embodiment, theontological query:

ONT: SELECT X.firstName, X.lastName

will get translated into the semantic data source query:

SELECT firstName, lastName FROM Partition_1, Partition_2, . . .,Partition_N. In addition, appropriate relations may be substituted withforeign-key/primary-key pairings when the query is translated into therelational form.

As discussed above, the ontological meta-model is comprises of classes1108, relations 1110 and attributes 1112. The sales ontology 1104comprises specific instances of the members of the ontology meta-model1102. For example, as shown, the sales ontology 1104 includes severalclasses including a person class 1114, a buyer class 1116 an employeeclass 1118, an invoice class 1120 and invoice item class 1121. As seenby it's name, the person class 1114 corresponds to people such asemployees, buyers and other people. Accordingly, the buyer class 1116and employee class 1118 are sub-classes of the person class 1114. Alsoclear from its name, the invoice class 1120 may be associated withinvoices and the invoice item class 1121 may be comprised of variousinvoice items such as various products sold by a business employing thedisclosed ontology. In an embodiment, the employee class 1118, invoiceclass 1120, and invoice item class 1121 have corresponding tables in thesemantic data store 1106. Other classes of the sales ontology 1104 mayalso have corresponding tables in the semantic data store 1106.

As shown, the sales ontology 1104 includes various relations from therelations 1110, such as a buyerOf relation 1122 and a sellerOf relation1124 and a hasItems relation 1126. The names of the various relationsalso may be related to their semantic meaning. For instance, as can beseen in the figure, a buyer of the buyer class 1116 may be related to aninvoice of the invoice class 1120 by the relation buyerOf because thebuyer may have purchased the particular items of the invoice. Likewise,an invoice of the invoice class 1120 is related to invoice items of theinvoice item class 1121 by the relation hasItems 1126 because theinvoice items were included on the invoice. Also, the sellerOf relation1124 relates an employee of the employee class 1118 to an invoice of theinvoice class 1120 when the employee was the person who sold the itemslisted on the invoice. In an embodiment, relations 1110 are representedin the semantic data store 1106 by the pairing of the primary key of thetables of the semantic data store 1106, as discussed below.

Further, various items of the sales ontology 1104 may include variousmembers of the attribute class 1112. As an example, person 1114 mayinclude a first name 1128 and a last name 1130, which, as indicated inthe drawing, may be stored as strings. Likewise, a buyer 1116 may have abuyerID unique to the buyer as may an employee 1118 have an employeeID1134 unique to the employee. Continuing this example, the invoice 1120may include an invoiceID 1136 unique to the invoice 1120 and a date1138, for example, on which the invoice 1120 was created. As a finalexample, an invoice item of the invoiceItem class 1121 may include anamount corresponding to the price at which the associated item was soldto the buyer 1116.

As discussed above, various items of the sales ontology are stored in asemantic data store 1106. In an embodiment, the semantic data store 1106may closely resemble a data store of another data model such as arelational database model. Thus, in an embodiment, the semantic datastore 1106 includes a plurality of tables where each table correspondsto a class of the ontology meta-model 1102. It should be understood,however, that the example semantic data store 1106 shown in the drawingsmay be in an intermediate format used to facilitate transformation ofthe data. Data from the semantic data store 1106 may be furthertransformed, for example, into a format suitable for use with aparticular reasoner operable to reason the data.

Thus, as shown in the illustrative example of FIG. 11, the semantic datastore 1106 includes an employee table 1142, an invoice table 1144 and aninvoice item table 1146. Each of the tables of the semantic data store1106 may include a key comprising an attribute unique to the entitiesrepresented by the table. For instance, the employee table 1142 mayinclude a column having each employee ID 1132. Other attributes may alsobe stored in tables such as the last name and first name attributes ofemployees in the employee table 1142. Likewise, the invoice table 1144may include a column corresponding to an invoice ID primary key,employee ID foreign key and buyer ID foreign key such that in thismanner, for example, an ID of an invoice may be located in the invoicetable 1144 and the employee associated with the invoice and buyer towhich items on the invoice are sold may be identified.

The above embodiments, and variations thereof, provide include featuresadditional to those discussed above. FIG. 12 shows an embodiment 1200 ofan environment in which embodiments of the invention may be practiced.As shown, the environment 12 includes a plurality of data stores 1202from which data is extracted and stored in a semantic data store 1204,such as in a manner described above. In an embodiment, data stored inthe semantic data store 1204 is reasoned by a reasoner 1206, which isoperated by a user of a user terminal 1208, in accordance with anembodiment. While FIG. 12 shows the reasoner 1206 analyzing data from asingle semantic data store 1204, the reasoner 1206 may utilize data frommultiple semantic data stores as well as from one or more of the datastores 1202, or data from other sources. Generally, the semantic datastore 1204 is a data store whose persistence mechanism can be the filesystem, database, or memory, depending on the usage within theapplication and, as described above, may be dynamically optimized forstorage of data based on deployed semantic domain definitions. Asdescribed above, semantic domain definitions may be OWL files thatencapsulate domain taxonomy, which may be defined by a business expert,for a particular domain.

In addition, it should be noted that FIG. 12 shows a simplifiedenvironment for the purposes of illustration, but that actualimplementations may utilize various components in addition to that whichis illustrated and, in various embodiments, certain components areomitted. For example, in an embodiment, the reasoner 1206 is implementedin one of several layers of a software architecture adapted forenforcing policies. As an example, an interface, such as a Web 2.0interface, may be provided for users to utilize various components.Customized interfaces for utilizing various components may be createdusing REST Web Services or services using other protocols, such as SOAP.One or more interfaces may be used to operate application services whichcoordinate components of policy enforcement, such as a policy enginethat operates the reasoner 1206 and the semantic data store 1204, dataservices that coordinate the transfer of data to the semantic data store1204, and report services that provide reporting documents based onanalysis of data in the semantic data store 1204, and the like. In anembodiment, the data services utilize Oracle Data Integrator 11gR1 andthe report services utilize Oracle Business Intelligence Publisher, bothavailable from Oracle Corporation.

As shown in the drawing, the reasoner 1206 comprises a plurality ofreasoning modules, where each reasoning module is configured to apply aset of rules to analyze data in the semantic data store 1204. While FIG.12 shows a certain number of reasoning modules of the reasoner 1206, thereasoner 1206 may have more or less reasoning modules. When reasoningdata, the reasoner may use all or some of its reasoning modules,depending on the type of reasoning being conducted. For instance, a usermay, through an input device, define a particular analysis that the userwould like to perform, and the reasoner may use one or more applicablemodules, such as in a manner described below. Further, a user may definehis or her own reasoning modules, which may include a combination ofsome of the reasoning modules of the reasoner 1206 directed to analyzedata in a particular manner.

In an embodiment, the reasoner includes one or more pattern basedreasoning modules 1210 (abbreviated as PBRM) and one or more semanticreasoning modules 1212 (abbreviated as SRM). In an embodiment, a PBRM1210 is a sub-reasoner of the reasoner 1206 that uses a predefinedprocess for performing statistical analysis on data from the semanticdata store in order to infer information from the data. PBRMs mayutilize range reasoning where data is looked at over a specified range,such as over a specified time period. As an example, utilizing amatrix-based approach, such as the approach described above, acovariance matrix of a vector may be constructed in order to measure howthe changes of variables in the vector depend on others. Likewise, thecovariance of two variables may be measured for other objects, such asmatrices or higher-dimensional objects. Correlation between twoseemingly random variables, such as between invoice amounts and paymentsunrelated to the invoices, may signify fraud. A PBRM may take as input aset of data, such as a sampling of numerical values (such as invoiceline items) over a time period, and may output conclusions based on astatistical analysis of the numerical values, such as covariancematrices or other objects.

Other statistical techniques may be used in PBRMs. For instance, patternrecognition may be used to identify activities that are out of theordinary. As an example, certain invoices, payments, and or other itemsmay be flagged for review if they contain an amount that is above orbelow a predefined threshold. As another example, pattern recognitiontechniques may be used to flag invoices, payments, or other items thatare not necessarily above or below a threshold, but that are otherwiseabnormal, such as invoice amounts that are larger or smaller than usual,but not outside of a range that would cause any flags to be set. Patternrecognition may also be used to compare activity with activity of thosehaving similar duties. For instance, pattern recognition may be used toidentify, through analysis of purchases and/or other data, that amanager of a location is replacing parts on equipment more frequentlythan managers of other locations. An investigation may be subsequentlymade to determine whether the manager is legitimately acting differentlyfrom his or her peers, whether corrective action needs to be taken,and/or whether fraud is being committed, such as by profiting off thesale of used parts.

Generally, techniques that may be employed in PBRMs include:cross-correlation analysis to discover the relationship between multipledependent variables; Bayesian filters to look at past events and buildprobabilistic models to predict future events to detect whether past,present, and/or future events violate a policy; and wavelets fordetection of data that is most likely to be suspect. Other techniquesmay also be used and, as new techniques are developed, a user may definereasoning modules that are able to apply any given technique. Forinstance, in an embodiment, users may define techniques that may beemployed by a PBRM using combinations of the above techniques and/ordefining additional techniques.

One or more SRMs 1212 may be used in connection with one or more PBRMsin order to increase the effectiveness of the modules. In an embodiment,an SRM is a reasoning module that applies one or more rules to a set ofdata, which may be put into matrix form, as described above, in order toprovide information about the relationships among the various data. Forinstance, a semantic reasoning module may identify all invoices relatedto a particular employee. Generally, use of SRMs and PBRMs providesincreased flexibility in choosing the data to be analyzed and thetechniques to be used for analysis. For instance, output of one or moreSRMs may be used as input for one or more PBRMs. As an example, if JohnDoe is an employee, a SRM may be used to identify invoices issued byJohn Doe, such as using any of the techniques, or variations thereof,discussed above. One or more covariance techniques may be used by one ormore PBRMs to determine whether there is a correlation between theinvoice amounts and other data, such as data not associated with JohnDoe. An SRM may be used to exclude data from the analysis that typicallywould be correlated to the invoice amounts, such as payments to thevendors identified on the invoices. An SRM may take input objects fromthe semantic data store, may construct appropriate matrices, and mayperform matrix operations on the matrices depending on the nature of thereasoning being performed, although matrices may be input into SRMs inother embodiments. Output from an SRM may be a set of inferences, orother conclusions, about the relationship among semantic data, or may bea set of numerical values (such as invoice line items), or other data.

Likewise, the output of one or more PBRMs may be used as input to one ormore SRMs. For instance, as discussed, PBRMs may be used to findcorrelations among various data. A SRM may be used to provide usefulinformation about data having correlations, such as people, roles,vendors, and others associated with a particular datum. This informationmay be viewed by an analyst who may decide whether to investigatefurther and/or take corrective action. Additionally, the information maybe used in order to define rules for additional analysis. The reasoner1206 may include additional logic to coordinate the flow of data amongreasoning modules being used, such as by formatting output of onereasoning module into a format suitable as input for another reasoningmodule. For instance, if an SRM outputs a set of inferences, thereasoner 1206 may extract from a semantic data store objects (such asnumerical values corresponding to objects associated with theinferences) and provide those values to a PBRM for processing by thePBRM.

As discussed in the preceding paragraphs, SRMs and PBRMs may be used inseries (where output of one or more modules is used as input for one ormore other modules). SRMs and PBRMs may also be used in parallel inappropriate circumstances. For instance, output of an SRM and output ofa PBRM may together be used as input for one or more other modules, eachof which may be an SRM or PBRM. Additionally, while the above discussionpertains to SRMs and PBRMs, other types of modules may be employed. Inan embodiment, one or more hybrid modules may be used in ways discussedabove, where a hybrid module is a reasoning module that employs bothsemantic reasoning (such as transitive reasoning of semantic data) andstatistical reasoning (such as pattern-based reasoning of numericaldata). A hybrid module may comprise a combination of one or more SRMsand/or PBRMs in series and/or parallel.

FIG. 13 illustrates steps of a method 1300 for reasoning data, inaccordance with an embodiment. As with any method disclosed herein, orvariations and/or combinations thereof, the method 1300 may be performedunder the control of one or more computer systems configured withexecutable instructions. The executable instructions may be embodied ona computer-readable medium. In an embodiment, at a data storage step1302, data is stored in one or more data stores, such as during normaloperations of an organization, as described above. As noted above, datamay be stored in a plurality of data stores that utilize various methodsof storing data, such as methods for storing data in a relationaldatabase, in flat files, and the like.

In accordance with an embodiment, at a semantic data storage step 1304,at least a portion of the data stored in the one or more data stores isstored in a semantic data store, such as a semantic data storeconfigured as described above. As discussed, storing data in thesemantic data store may involve the use of various filters in order toexclude some data from the one or more data stores and also may involvethe use of various transformations of the data that put the data in aform suitable for storage in the semantic data store, such as in amanner described above. In addition, while the method 1304 describes asingle semantic data store, more than one semantic data store may beutilized.

At a semantic reasoning step 1306, in an embodiment, data from thesemantic data store is reasoned using a SRM, where the SRM may be asdescribed above. For instance, a SRM may apply transitive reasoning todata in the semantic data store in order to identify relationshipsspecified by a user of a system employing the method 1300, such as allinvoices associated with a particular employee and/or having particularattributes. Semantic reasoning may include construction of one or morematrices or other objects whose entries signify something in the data,such as an amount, or a 0 or 1 as described above. Once the matrix ormatrices are constructed, semantic reasoning may include applying matrixoperations and/or other analysis to the matrices, depending on theparticular type of reasoning being performed. At a statistical reasoningstep 1308, data from the semantic data store is reasoned using a PBRM,in accordance with an embodiment. For instance, a PBRM may applystatistical reasoning to data specified by a user, such as to particularinvoice values for the invoices identified by the SRM. As with thesemantic reasoning step 1306, the statistical reasoning step may includeconstruction and/or operations and/or other analysis on one or morematrices whose entries have a significance to the data.

While the method shows the semantic reasoning step 1306 performed beforethe statistical reasoning step 1308, the steps may be performed inanother order or at the same time. For example, a PBRM may be used toidentify suspicious values in the Semantic data store and a SRM may thenidentify employees and other semantic objects associated with thesuspicious values. Further, also described above, a plurality of SRMsand/or PBRMs may be used to reason data in the semantic data store andmay reason data in series and/or in parallel. Also, reasoning modulesother than SRMs and PBRMs may be used as well. In order to providecustomizability and/or scalability, each reasoning module may beadaptable to receive as input from other reasoning modules. Forinstance, operations in an embodiment where matrices are used, such asthose described above, the dimensions of a matrix output by a reasoningmodule are used by another reasoning module so that operations on thematrix by the other reasoning module proceed properly. The dimensionsmay vary based on the amount or other characteristics of data beingreasoned.

In an embodiment, at a results step 1310, results of the reasoning areprovided to the user. Providing the results may include causing thedisplay of information corresponding to the results through a graphicaluser interface of the system. The results may be presented in variousforms which may employ text, graphics, video, audio, and other features.For example, graphs that illustrate statistical relationships betweensemantic objects may be displayed, as may text describing therelationships.

FIG. 14 shows a method 1400 for directing reasoning of semantic data, inaccordance with an embodiment. Steps of the method shown in FIG. 14 maybe used, for example, to allow a user to specify which data should beanalyzed and which reasoning modules should be used. Thus, steps of themethod shown in FIG. 14 may be used to allow a user to specify how themethod shown in FIG. 13 (or variations thereof) should be performed. Themethod 1400 may be performed pursuant to interaction of a user with agraphical user interface, such as an interface having one or more of thefeatures described below, where the interaction may involve use by theuser of one or more input devices. At a data identification step 1402,data is identified in accordance with an embodiment. For instance, auser may specify, through an interface, data in which he or she isinterested and, based at least in part on input from the user,appropriate data is identified. Identifying data may include specifyingtypes of semantic objects, such as invoices, and identifyingcorresponding data in the semantic data store, such as specific invoicescreated by employees of an organization. One or more attributes ofsemantic object types may also be specified, such as ranges for valueson invoices, employees or a certain rank, and the like.

In an embodiment, at a SRM selection step 1404, an SRM is selected.Selection of the SRM may be based at least in part on user input, whichmay be received during performance of the data identification step 1402.For example, if a user specifies that he or she would like to analyzeall invoices belonging to a particular employee or group of employees,an SRM configured to identify invoices associated with the employee(s)may be selected. At a PBRM selection step 1406, in an embodiment, a PBRMis selected. As with the SRM, selection of the PBRM may be based atleast in part on user input. For example, if a user specifies that he orshe would like to analyze the correlation between invoice values andother semantic objects, a PBRM operable to perform this analysis may beselected. For instance, a PBRM that constructs a covariance matrix fromvectors in a matrix constructed in accordance with the above descriptionmay be selected.

While FIG. 14 shows the SRM selection step 1404 occurring before thePBRM selection step 1406, the steps may be performed in another order orat the same time. In addition, as discussed above, embodiments of thepresent invention provide for scalability. Accordingly, the method 1400may include selection of a plurality of SRMs and/or a plurality ofPBRMs. In other embodiments, one or more SRMs are selected but no PBRMsare selected. Likewise, only one or more PBRMs may be selected withoutselection of any SRMs.

At a data reasoning step 1408, the identified data is reasoned accordingto the selected SRMs and PBRMs, in accordance with an embodiment.Reasoning the data may include applying any selected SRMs and PBRMs inan order that is based at least in part on user input. At a results step1410, results of the reasoning are provided, such as in a mannerdescribed above.

As discussed above, users may interact with an interface in order todefine the way in which data is analyzed in order to ensure compliancewith one or more policies. As an example, a user may interact with aninterface in order to define how to detect whether fraud is beingcommitted or is potentially being committed. In an embodiment, usersspecify parameters that define how analysis of data is to take place.Parameters may be defined using semantic concepts, such as employee,invoice, line item, and the like. The interface may operate according toexecutable instructions embodied on a computer-readable storage medium.

As an example, in accordance with an embodiment of the presentinvention, FIG. 15 shows an example graphical representation 1510 oflogic for an analysis to be performed as part of implementation of apolicy related to credit card charges, specifically a policy relating tocredit card charges over $500. Upon receipt of instructions from a user,a system performing analysis of data according to the graphicalrepresentation 1510 may direct a semantic reasoning module to identifyfrom a semantic data store credit card charges on company credit cardsthat are for amounts greater than $500. Identification of the creditcard charges may be performed according to techniques described above,or other techniques. This graphical representation may be used, forexample, for the implementation of a business policy specifying that allcredit card charges greater than $500 require approval from a specificperson or class of persons, such as managers. Identification of suchcredit card charges using the policy allows for implementation of thebusiness policy.

The graphical representation 1510 includes a credit card charges object1512 that includes a plurality of options for specifying data that maybe related to credit card charges. The specified data may be identifiedduring analysis performed during implementation of the policy. Forexample, a date checkbox 1514 allows users to specify, by checking thedate checkbox 1514, that credit card charges identified duringimplementation of the policy will include date information about thedate on which the charge was made or recorded. Likewise, a descriptioncheckbox 1516 and an amount checkbox 1518 allow users to specify thatcredit card charges identified during implementation of the policy willinclude a stored description of each charge and/or an amount of eachcharge, respectively.

In various embodiments, users are able to specify various criteria sothat implementation of a policy results in the identification ofinformation matching or closely matching the criteria. For example,continuing the example of FIG. 15, the credit card charges object 1512includes an amount comparison field 1520 that allows users to specifythat credit card charges that are to be identified by implementation ofthe corresponding policy should have amounts matching certain criteria.In the example shown, the amount comparison field 1520 includes anamount condition dropdown box 1522 and an amount entry field 1524 whichcollectively allow a user to enter an amount into the amount entry field1524 and specify, using the amount condition dropdown box 1522, whatamount identified credit card charges should have in comparison with theamount entered into the amount entry field 1524. In the example shown, auser has selected that each credit card charge identified by the policyshould have an amount greater than 500 dollars. In various embodiments,credit card charges not matching the selected criteria may be identifiedby implementation of a policy, such as when few or no results match theselected criteria. For example, continuing the example shown, ifimplementation of the policy corresponding to the graphicalrepresentation 1510 does not result in any credit card chargesidentified, credit card charges having amounts less than but near 500may be included. In accordance with an embodiment, fields may be addedto or removed from graphical objects, as appropriate.

As noted above, various objects may be associated with one another, forexample by graphically linking the objects together, for variouspurposes. For instance, the graphical representation 1510 includes anemployee object 1526 which, in the example shown FIG. 15, has beenassociated with the credit card charges object 1512 by connecting theobjects together with a line. In this specific example, the results ofassociating the employee object 1526 with the credit card charges objectare that a credit card charge identified by implementation of the policyincludes information identifying the person who made the charge, such asthe name of the employee which may be specified by a name checkbox 1528of the employee object 1526. Thus, when the policy associated with thegraphical representation 1510 is executed, an SRM may identify from asemantic data store credit card charges having the specified properties.A second SRM may take the identified credit card charges as input andidentify names of the employees that made the identified charges. Thecredit card charges and names may be associated with one another in oneor more data records, such as in a table in a relational database. Also,the credit card charges and names may be displayed to a user (such as ina table, spreadsheet, or other format) and/or may be used as input intoanother reasoning module, such as a PBRM or another SRM, depending ondirections from a user.

As seen in the drawing, the credit card charges object includes a“delete” button, a “test” button, and a “save” button. Other elements ofan interface employing embodiments of the present invention may includethese buttons, and/or similar buttons or other elements that perform thesame and/or similar functions. In an embodiment, the “delete” buttonallows a user to delete the policy, thereby disallowing access to thepolicy and/or removing the policy from computer memory. The “test”button, in an embodiment, allows a user to analyze data according to theparameters that he or she specified. For instance, selection of the“test” button in FIG. 15 may cause a computer system to analyze data ina semantic data store in order to identify credit card charges greaterthan $500 and display the identified charges with their associateddates, descriptions, and amounts. As discussed above, data in thesemantic data store may be stored in a relational database. In thisinstance, selection of the “test” button may result in appropriatequeries being made to the database to identify data according to thedefined analysis. Further, one or more matrices may be defined andappropriate matrix operations may be performed. Also, selection of the“test” button may cause only a portion of a data store to be analyzed,which may be useful when complete analysis would take a long time due tothe size of the data store but the user simply wants to determinewhether the analysis that he or she designed results in desiredinformation being returned.

Moving on to the “save” button, in an embodiment, the “save” buttonallows a user to save the graphical representation, or other informationcorresponding to the graphical representation, in computer memory, whichmay be non-volatile. The policy may be saved in memory as a set ofinstructions that instruct a computer system to perform an analysis ofthe data according to specified parameters. A user may access a savedanalysis from memory and analyze data according to the policy and/or mayutilize the policy in connection with other policies. For instance, auser may utilize techniques described herein in order to use a policy asa component in another analysis and/or to modify the analysis.

FIG. 16 shows another example of a graphical representation 1630 createdby a user in order to create an analysis, in accordance with anembodiment. The graphical representation 1630 includes a purchase orderobject 1632 which has features similar to the credit card charges object1610, described above. For example, the graphical representation 1630includes a plurality of checkboxes 1634, the selection of whichspecifies information to be included with purchase orders identified byimplementation of the policy.

The graphical representation 1630 shown also includes fields forselecting criteria for various pieces of information associated withpurchase orders. Boolean operators are also included in order to providesubstantiality for how the criteria are selected. For example, in theexample of FIG. 16, an amount comparison field 1636 and a line itemcomparison field 1638 are selected and connected together with an ANDoperator so that criteria selected with the amount comparison field 1636and with the line item comparison field 1638 both must be matched orclosely matched during implementation of the policy. Other operators,such as OR operators or other Boolean or non-Boolean operators may alsobe included. Various controls for arranging fields, such as a deletecontrol 1640 for deleting fields, may be included to provide robustfunctionality for creating policies. Other controls, such as a “test”control 1642 for testing a created analysis, and a “save” control 1644for saving an analysis, may also be included.

In accordance with various embodiments, other features are included foruser-definition of analyses performed in connection with implementationof policies. For instance, in accordance with an embodiment, variousgraphical objects corresponding to data analysis techniques are includedso that a user may include one or more of the graphical objects into agraphical representation of an analysis to be performed as part ofimplementation of a business policy so as to indicate that the dataanalysis technique should be applied during implementation of thepolicy. As an example, an icon representative of an algorithm fordetecting micropayment fraud may be placed onto a graphical object, suchas an object representative of an invoice, to indicate that thealgorithm should be applied A plurality of graphical objectsrepresentative of commonly-used data analysis techniques may be includedfor selection by a user. In addition, users may create their own dataanalysis techniques or modify and/or combine data analysis techniques inorder to create custom data analysis techniques.

Accordingly, FIG. 17 shows an example of an interface 1700 of a tool fordesigning analyses used during implementation of policies, in accordancewith an embodiment. The interface may behave according to executableinstructions embodied on a computer-readable storage medium. In theexample shown, the interface 1700 is divided into three verticalcolumns, although other arrangements are possible and, in an embodiment,the arrangement is changeable according to user input. As shown, themiddle column is labeled as “Work Space” and the right column is labeledas “Pattern Palette.” Also as shown, the left column includes two rows,the upper row being labeled as “Predefined Semantics” and the secondbeing labeled as “Custom Semantics.” In the Predefined Semantics row, aplurality of semantic elements are provided as selectable interfaceelements. In an embodiment, the predefined semantics may includegraphical representations of semantic objects that are provided for usewith common software programs. For instance, in the example shown,predefined semantics are included for E-Business Suite application(EBS), PeopleSoft (PSFT) available from Oracle Corporation, andQuickbooks Enterprise available from Intuit, Inc. As shown in theexample, predefined semantics may be provided through interface elements(shown as cubes having a plus sign or a minus sign) which may beselected to show the predefined semantics for the associated software(by selecting a cube with a plus sign) or to hide the predefinedsemantics for the associated software (by selecting a cube with a minussign).

In an embodiment, the semantic elements in the Predefined Semantics rowcorrespond to data items that are commonly used when enforcing policies.For instance, in the example shown, the semantic elements associatedwith EBS include a customer element, an employee element, and invoiceelement. Elements may also include sub-elements. For instance, in theexample shown, the invoice element includes elements commonly associatedwith invoices, such as a line item sub-element, a purchase ordersub-element, a sales person sub-element, and a vendor sub-element.

A user may interact with the elements on the interface 1700 in variousways. For instance, a user may use a mouse or similarly operationalinput device to select an element and drag the element into theWorkspace column (i.e. the middle column labeled as “Work Space”). Upondropping the item into the Workspace column (for instance by releasing amouse button), a box corresponding to the element may appear in theWorkspace column. For example, an Invoice box 1702 may appear in theWorkspace column upon dragging and dropping an Invoice element from thePredefined Semantics row of the left column into the Workspace column.The box may include elements associated with invoices, as describedabove.

In the Custom Semantics row of the left column, in an embodiment, theinterface may include one or more elements (tools) that allow a user todefine custom semantics, such as by labeling items in a data store thatdo not correspond to any of the predefined semantics or that docorrespond to one of the predefined semantics, but where thecorrespondence is not automatically recognized. Users may also definecustom semantics using the tools provided in order to define analysisfor policies, such as in a manner described above. In the example shown,the custom semantics includes two categories of custom semantics, a“Mappings” category and an “Entities” category. In an embodiment, theMappings category includes tools for mapping data from various datasources to semantic objects. For instance, as shown, the Mappingscategory includes a flat-file mapper for mapping data from flat-files, aRDBMS mapper for mapping data from relational databases, and a custommapper for mapping data from other data sources. Each of the mappers,when selected, may provide an interface for identifying data from one ormore data sources. Software providing the interface may utilize an APIof the data source in order to gain access to the data and the interfacemay allow developers to input commands, according to an API, which arenot pre-loaded with the software. As an example, software providing aninterface of the RDBMS mapping tool may utilize the API of a particularRDBMS to gain access to tables of a relational database. A user mayspecify, for example, that data in a particular column of a particulartable correspond to a particular semantic object. For instance, the usermay specify that data in a column identify customer names. In anembodiment, once mappings are made using any of the tools in theMappings category, the mappings may be saved and semantic entitiesmapped to data sources may appear appropriately in the PredefinedSemantics row.

Tools in the Entities category, in an embodiment, provide for buildinganalyses for policies using various semantic objects. For instance, apredicate tool, in an embodiment, allows one to specify an associationbetween two semantic entities such that, when data is analyzed accordingto an analysis that has been defined, data that has the specifiedassociation is identified. Graphically, the predicate tool connects twographical objects representative of semantic entities with a line orother device representative of an association. In the example shown inthe Work Space column, an Invoice object is connected to a Sales Personobject with a line and the Sales Person object specifies the name ofsales person. In this manner, when data is analyzed according to theexample arrangement of graphical objects defined in the Work Spacecolumn, invoices that are identified will be associated with a salesperson (or perhaps several sales people) whose name is Bob. In anembodiment, if the checkbox next to “name” in the Sales Person graphicalobject is not checked, then invoices would be identified as well assales people associated with the identified invoices, regardless oftheir name. In a similar manner, a Lineitem graphical is shown asconnected to the Invoice graphical object with a line, therebyspecifying that lineitems for identified invoices should be identified.In this manner, a user may specify the types of information he or shewould like to view in connection with any identified invoices.

Another tool in the Entities category, in an embodiment, is a Group toolwhich, allows a user to specify that certain semantic objects are partof a group such that one or more actions may be taken with respect tothe group. In an embodiment, the Group tool allows users to graphicallysurround a plurality of graphical objects in order to specify thatsemantic objects represented by the graphical objects are part of agroup. For instance, in the Work Space column, the Sales Persongraphical object and the Lineitem graphical object are surrounded by arectangle having a dashed border, thereby indicating that sales peopleand line items applicable to the defined analysis are part of a group.In the example shown, an icon labeled FHT has been superimposed onto theborder defining the group, indicating that a Fast Hough Transform (FHT)should be computed for the data associated with the grouped graphicalobjects. In an embodiment, the FHT icon is superimposed onto the borderof the group through a drag and drop operation by a user from anotherlocation on the screen, as described below, although any type of userinteraction with the interface may be used in addition, or as analternative to a drag and drop. Further, the FHT icon (or any of theother icons that may be used, described more completely below) may beassigned to a group through other actions, such as by a user indicating(perhaps through a drag and drop) that the FHT icon should appear on theborder of the group, in the space surrounded by the group, or throughany other specified user action.

Also in the Entities category, in an embodiment, a Classifier toolallows users to define new semantic entities or to modify existingsemantic entities. For example, if a company sells widgets, “widgets”may not appear as a predefined semantic entity, but it may wish todefine one or more analyses that utilize data related to its widgets. Inan embodiment, upon selection of the Classifier tool, the user isprovided with an opportunity to create or modify a semantic entity.Creation and/or modification of the semantic entity may involveproviding a name to the entity and specifying which attributes theentity should have. In addition, a user may be able to define the datatypes of the attributes of a semantic entity (such as integer, double,string, and the like) and/or the data types may be determined based on amapping of the semantic entity to a data source (which may be completedusing one of the mapping tools discussed above). For instance, if acolumn in a RDBMS contains integers and that column has been mapped toan attribute of a created entity, then the attribute of the semanticentity may automatically be assigned an integer data type.

As discussed above, various types of statistical analysis may beperformed for data represented by graphical objects. In an embodiment,the Pattern Palette includes a plurality of graphical icons, eachrepresentative of a type of analysis that may be performed. Forinstance, as discussed above, the pattern palette includes an FHT iconfor performing Fast Hough Transforms. In addition, a Calculator tool1702 may be provided for performing more simple analysis, such asaddition, subtraction, multiplication, division, and the like, amongdata corresponding to one or more of the graphical objects in the WorkSpace column. For instance, the Calculator tool may be used to identifythe difference between list prices and sale prices for items identifiedaccording to an analysis defined in the Work Space column. Ifapplicable, such as with the Calculator tool, a user may be providedcontrols that allow the user to select or otherwise define how the toolbehaves. The controls may be provided automatically upon selection ofthe tool or may be provided upon one or more specified user actions withthe graphical icon representative of the tool and/or other interactionswith the interface.

The Pattern Palette, or other portion of a user interface, may includeother graphical representations of analyses that may be performed ondata represented by graphical objects selected and/or grouped by theuser. For example, graphical representations, such as icons or otherobjects, may be provided for each of the statistical analyses discussedabove and/or for user-defined analyses. Further, in another embodiment,a user may group graphical representations of semantic objects in theWork Space without using the Grouping tool discussed above by dragging agraphical representation of an analysis around the graphicalrepresentations to be grouped, or in other ways.

FIG. 18 shows a method 1800 for creating policies, in accordance with anembodiment. The method shown in FIG. 18, or variations thereof, may beimplemented by software (e.g., code, instructions, program) executing ona processor, by hardware, or combinations thereof. The software may bestored on a computer-readable storage medium, for example, in the formof a computer program comprising a plurality of instructions executableby one or more processors.

In an embodiment, the method 1800 includes providing a graphical objectsrepresentative of semantic objects to a user at an object providing step1802. For example, one or more computer systems may cause display of agraphical user interface that a user may interact with using an inputdevice of the computer system(s) in order to cause the graphical objectsto appear and/or the interface may include a plurality of displayedgraphical objects that the user may select and/or move using the inputdevice. The graphical objects may be similar to those illustrativeexamples described above, although their appearance may vary. Inaddition, graphical objects representative of particular types of dataanalysis, such as those described above, may be provided as well.

In an embodiment, at an arrangement receipt step 1804, an arrangement ofgraphical objects is received. Receiving the arrangement of graphicalobjects may include receiving a series of commands from the user via theinput device, where the series of the commands indicates which objectsare received and how they are graphically arranged on a display deviceof the user. For instance, referring to the illustrative example of FIG.17, the graphical arrangement of objects in the Work Space column mayhave been produced according to user interaction with the interface.Receiving the arrangement of graphical objects may also includereceiving data that indicates how the graphical objects have beendirected to be arranged by the user. In addition, the arrangement mayinclude one or more graphical objects representative of particular typesof analysis, such as pattern or other statistical analysis, as describedabove.

At a conversion step 1806, in an embodiment, the arrangement isconverted to executable instructions for performing analysis that may beimplemented, such as in a manner described above. For instance,executable instructions for execution by an application may be generatedbased at least in part on the arrangement. Conversion of thearrangement, in an embodiment, includes identifying a set of conditionsfor data fulfilling the conditions to be identified upon execution ofthe policy, such as data within specified amounts and/or data associatedwith semantic classes or specific semantic entities. Also, conversion ofthe arrangement may include construction of executable instructions forimplementing the policy based at least in part on the arrangement.Conversion of the arrangement may also include identification of one ormore actions to be taken for data that fulfill the conditions, such asdisplay of the data in one or more formats, messages to be sent tospecified people and/or to be displayed, and the like.

FIGS. 19A-19B show various graphical interface pages of an interfacewhich may be used in practicing one or more embodiments of theinvention. FIG. 19A, for example, shows a page 1900 for creating models,where a model is a graphical representation of an analysis to beperformed on data according to the model. The model, for instance, mayrepresent one or more conditions (often referred to as a policy) suchthat data fulfilling or violating the conditions may be identified. Inthe example shown in FIG. 19A, the page 1900 includes a left pane 1902and a right pane 1904. As will be described in more detail, the leftpane includes a list of semantic objects that can be placed (by adrag-and-drop, double click, or other user interaction with theinterface) by a user into the right pane 1904. In the example shown,each semantic object in the left pane includes a description of ageneral category into which the object is classified. For instance, theleft pane 1902 includes a “Payment” object that is shown as belonging toa “Financials” category. Other categories may include “Human Resources,”“General Ledger,” “customer relationship management” and generally anyother category into which semantic objects may be classified.

In an embodiment, the right pane 1904 includes three sub-panes: a modelobjects sub-pane 1906, a model logic sub-pane 1908, and a resultsdisplay sub-pane 1910. In an embodiment, each of the sub-panes areexpandable and contractible by appropriate interaction with the page1900 (such as by clicking a “+” to expand or a “−” to contract). Asshown in FIG. 19A, all three sub-panes are in a contractedconfiguration.

FIG. 19B shows a page 1912 in which the model objects sub-pane 1906 andmodel logic sub-pane 1908 are in an expanded configuration, therebyoccupying more space on the page 1912 than as shown in FIG. 19A. In anembodiment, the model objects sub-pane 1906 is used to designate whichsemantic objects and attributes of those objects will be considered fora model that is constructed in the model logic sub-pane 1908. Boxes orother representations (referred to as model objects) of semantic objectsmay be located in the model objects sub-pane as a result of variousactions, such as a drag-and-drop of a semantic object from the left pane1902 to the model objects sub-pane 1906, the opening of a file storinginformation indicative of specific representations of semantic objectsbeing in the model objects sub-pane, or other actions.

As shown in FIG. 19B, the model objects sub-pane includes two modelobjects indicated by boxes (although other shapes may be used), apayment box 1914 and a payment card box 1916. One or more of the modelobjects in the model objects sub-pane 1906 may include any attributesassociated with semantic model objects represented by the model objects.For instance, the payment box 1914 includes attributes associated withpayments, such as “amount,” “bank account ID,” “bank account Name,” andothers. The payment card box 1916 (which may represent credit, debit, orother payment cards), for example, includes attributes such as “activeflag” (indicating whether a credit card is active or inactive), “cardholder name,” “card issuer code,” and others.

Turning to the model logic sub-pane 1908, the model logic sub-pane, inan embodiment, is a portion of the interface where graphical objects maybe manipulated in order to define logic by which data analysis shouldproceed. Graphical objects may appear in the model logic sub-pane 1908by clicking or otherwise selecting icons associated with the objects.For example, in the example of FIG. 19B, a first filter object 1918 mayhave appeared in the model logic sub-pane 1908 as a result of a userselecting a filter icon 1920. A filter object, in an embodiment, is agraphical object that represents or that can be manipulated to representa semantic object. A filter object may also represent or be able to bemanipulated to represent one or more conditions for data associated withsemantic objects. For instance, the first filter object 1918 shown inthe example of FIG. 19B represents payments that have an amount that isless than a specified value (where a user has yet to input a value, asshown in the figure).

As noted, in an embodiment, filter objects can be manipulated torepresent semantic objects. In an embodiment, a filter object mayrepresent any of the semantic objects represented by objects in themodel objects sub-pane 1906. For example, as shown in the figure, asecond filter object 1922 includes a drop-down box 1924 that allowsusers to select which semantic object is represented with the secondfilter object. In the example shown, a user may select the second filterobject 1922 to represent payments or payment cards because thosesemantic objects are represented in the model objects sub-pane 1906.Thus, if another model object was in the model objects sub-pane 1906, asemantic object represented by that model object would also appear inthe drop-down box 1924, in an embodiment.

In an embodiment, filter objects may include other drop-down boxes orother interface objects that are appropriate to objects or attributesthat have already been selected and/or specified. Available drop-downboxes or other elements may dynamically become available as a userinteracts with other elements of a filter object. For instance, as shownin FIG. 19B, the first filter object 1918 includes an attributedrop-down box 1926. In an embodiment, the attribute drop-down box 1926allows users to select one or more attributes associated with theselected object. Thus, as shown, the attribute drop-down box 1926includes attributes associated with payments, since payments have beenselected to be represented by the first filter object 1918. As shown inthe drawing, each filter object may include other drop-down boxes andother interface elements that are appropriate to selections and inputsalready made by a user.

In an embodiment, the graphical arrangement of objects in the modellogic pane 1908, as determined by a user, is representative of howanalysis of data should proceed. For instance, as shown in FIG. 19B, thefirst and second filter objects 1918, 1922 are vertically connected,where a vertical connection indicates an AND operation. Thus, for datato be identified by an analysis that proceeds according to the modelshown in FIG. 19B, the data must satisfy (or violate, in an embodiment)the conditions set by the first filter object 1918 and second filterobject 1922. A horizontal connection, as shown in FIG. 19C, indicates anOR operation such that data identified by an analysis performedaccording to the model shown in FIG. 19C must satisfy (or violate, inanother embodiment) conditions specified by the first filter object 1918or second filter object 1922. In addition, FIG. 19C shows a third filterobject 1928 vertically connected to the first filter object 1918 andsecond filter object 1922, thereby indicating that data identified bythe analysis should satisfy the conditions of the first filter object1918 or the second filter object 1922 in addition to the conditions ofthe third filter object 1928. Thus, as shown in the drawing, an analysisperformed according to the model of FIG. 19C should identify paymentsthat are greater than $86 or less than $5, but that are made by a personnamed Bob. Thus, embodiments of the present invention provide anintuitive and flexible way for users to define conditions for data thatcan be analyzed according to the conditions.

As discussed above, various pattern recognition techniques may beutilized. FIG. 19D shows an example of how pattern recognitiontechniques may be used in accordance with various embodiments. As shownin the drawing and discussed above, the model objects sub-pane 1906includes only the payments box 1914, indicating that objects in themodel logic sub-pane 1908 should represent or should be able to bemanipulated in order to represent payments and/or attributes associatedwith payments, in accordance with an embodiment. Also shown, in anembodiment, the model logic sub-pane 1908 includes a pattern object1930. The pattern object 1930 may have appeared in the model logicsub-pane 1908 in response to a user having selected a pattern icon 1932,although the pattern object may appear in response to other actions.

A pattern object, in an embodiment, allows users to set conditions forone or more statistical analyses to be performed in connection with oneor more semantic objects represented in the model objects sub-pane 1906.For instance, FIG. 19D only shows payments being represented in themodel objects sub-pane 1906 and, as a result, the pattern object 1930 islabeled in a manner indicating that the pattern object is representativeof statistical analysis of payments. If more objects had been present inthe model objects sub-pane 1906, in an embodiment, the pattern object1930 may have included a drop-down box or other mechanism for allowinguser selection of the semantic objects represented in the model objectssub-pane 1906.

In an embodiment, the pattern object 1930 allows selection of variouspattern recognition techniques or other statistical analyses that areapplicable to a semantic object represented by the pattern object. Forinstance, as shown in FIG. 19D, the pattern object 1930 includes adrop-down box 1932 that allows selection of statistical analysesapplicable to payments. As shown, the drop-down box 1932 allowsselection between Benford analysis or analysis involving mean paymentvalues. Thus, for instance, a user may select “Benford” in the drop-downbox 1932 in order to specify, using the pattern object 1930, paymentsthat deviate from a mean.

In an embodiment, pattern objects also are able to be manipulated inorder to set conditions for selected statistical analyses. As anexample, as shown in FIG. 19D, the pattern object 1930 includes aconditions area 1934 that allows user setting of conditions for theselected Benford analyses. Specifically, users are able to use drop-downboxes to set the amounts by which payments should be above or below amean. The options available in the conditions area 1934 may be differentdepending on the type of analysis a user has chosen for the patternobject. For instance, if a user had selected an analysis different froma Benford analysis, the settable conditions in the conditions area 1934would have been different in order to be applicable to the specificanalysis selected. The settings available in the conditions area maydynamically change as other portions of the pattern object are selectedand/or otherwise specified.

Pattern objects in the model logic sub-pane 1908 may be combined withother objects, such as filter objects in order to specify a moresophisticated set of conditions for analysis. For example, a filterobject representative of payments may be vertically connected to thepattern object 1930 in order to specify other conditions for paymentsidentified by the analysis. Thus, a user may vertically connect apayment filter object to the pattern object 1930 in order to specifythat identified payments should be greater than an amount specified bythe payment filter object. Horizontal connections of filter objects topattern objects may specify an OR operation, as described above. Otherobjects may also be used in the model objects sub-pane 1908 andconnected to other objects vertically or horizontally (to specify AND orOR operations, respectively, in an embodiment), or otherwise. Inaddition, in an embodiment, other tools are available for modeldefinition in order to provide a wide range of user options for definingconditions for analysis.

For example, in an embodiment, the model logic sub-pane 1908 includes anew function icon 1936 which may be selected by a user to perform afunction with respect to data associated with objects in the model logicsub-pane. Example functions are addition, subtraction, multiplication,division, square or other roots, exponentiation, trigonometricfunctions, and generally any function that may be performed inconnection with data. Selecting the function icon 1936 may cause agraphic to appear in the model logic sub-pane 1908 where the graphicallows a user to select a function applicable to available objects. Asan example, a user may select a subtraction function in order tosubtract payment amounts from invoice amounts. Further, functions neednot be limited to operations on numerical values, but may perform otheroperations, such as concatenation of strings and others.

In an embodiment, the model logic sub-pane also includes a grouping icon1938 and an expand icon 1940. The grouping icon 1938, in an embodiment,allows users to select a group of objects in the model logic sub-pane1908, such as by using an input device (e.g. mouse, touch screen, orother device) to enclose some or all of the objects in the model logicsub-pane. A selected group of objects can be replaced with a singlegraphical object (or simply fewer graphical objects). Thus, for example,a user may, by using the grouping icon 1938, group all of the objectsshown in the model logic sub-pane of FIG. 19C and replace the group ofobjects with a single object. In an embodiment, users may provide namesfor groups of objects and may be able to change settings for the wholegroup. Thus, complex models in the model logic sub-pane 1908 may besimplified by grouping portions of the models. In an embodiment, theexpand icon 1940 may be used in order to change a graphical objectrepresentative of a group of objects to the objects represented. Forexample, a single object representative of the model shown in FIG. 19Cmay be replaced with the plurality of objects shown in the model logicsub-pane 1908 of the figure through use of the expand icon 1940. Thegraphical appearance of any model in the model logic sub-pane may bealtered in order to accommodate extra space occupied by an expandedgroup of objects or by less space occupied by a grouped group ofobjects.

Turning to FIG. 19E, the figure shows an example of the results displaysub-pane 1910. In an embodiment, the results display sub-pane 1910includes graphical objects representative of data identifiable throughanalysis of data according to the conditions set by a model in the modellogic sub-pane 1908. For instance, in the example of FIG. 19E, theresults display sub-pane 1910 includes an available objects box 1942which, in an embodiment, identifies the semantic objects represented byobjects used in the model logic sub-pane 1908 and/or model objectssub-pane 1906. As shown in the figure, the available objects box 1942identifies the payment and payment card semantic objects, for instance,because objects representative of payments and payment cards were usedin the model logic sub-pane 1910.

In an embodiment, the available objects box 1942 shows the word “PaymentCard” with attributes associated with payment cards listed under“Payment Card.” The available objects box 1942 also shows the word“Payment” without any payment attributes listed. In the example shown, auser may cause the display of payment attributes by selecting the “+”next to the word “Payment.” Similarly, a user may hide the payment cardattributes by selecting the “−” next to the phrase “Payment Card.” In anembodiment, when attributes are displayed, a user may select from theattributes shown in the available objects box 1942 in order to specifywhat kind of data will be shown with data that is identified by analysisperformed according to the logic specified in the model logic sub-pane1908. For instance, the model logic may specify logic for analysis thatidentifies payment cards that satisfy certain conditions. However, whenidentifying such payments, other information may be of interest, such asthe names of employees that are identified as holders of the identifiedpayment cards. If all information associated with payment cards wasidentified in the results of analysis, it may be confusing to users whowould have to sift through a lot of unwanted information. In thismanner, results presented to users are more useful to the users. Inaddition, better performance may be achieved as a result of a reductionof the amount of data involved in the analysis.

In the embodiment illustrated in the figure, a user specifies attributesfor display with analysis results by selecting the attributes in theavailable objects box 1942 and subsequently selecting a right-pointingarrow between the available objects box 1942 and a selected attributesbox 1944. Selected attributes then appear in the selected attributes box1944, in an embodiment. Attributes that have been selected may bede-selected by selecting one or more attributes in the selectedattributes box 1944 and selecting a left-pointing arrow between theavailable objects box 1942 and the selected attributes box 1944.

In an embodiment, the results display sub-pane 1910 includes a viewresults icon 1946 that allows a user to select the icon in order tocause a computer system to perform the analysis specified in the modellogic sub-pane 1908. Results may be presented in a variety of formats,such as a table with a column for each attribute selected in the resultsdisplay sub-pane 1910, line graphs, bar graphs, and generally any formatthat is useful to users depending on the analysis performed.

Variations of the embodiments shown in FIGS. 19A-19E may include otherfeatures in addition to those described above or fewer features thanshown. Also, features such as those described above in connection withFIGS. 15-18 may also be incorporated into embodiments that takeadvantage of features of the embodiments described in connection withFIGS. 19A-19E. Also, while the drawings show examples of graphics thatmay be used in accordance with various embodiments, different graphicsmay be used.

Generally, although specific embodiments of the invention have beendescribed, various modifications, alterations, alternativeconstructions, and equivalents are also encompassed within the scope ofthe invention. Embodiments of the present invention are not restrictedto operation within certain specific data processing environments, butare free to operate within a plurality of data processing environments.Additionally, although embodiments of the present invention have beendescribed using a particular series of transactions and steps, it shouldbe apparent to those skilled in the art that the scope of the presentinvention is not limited to the described series of transactions andsteps.

Further, while embodiments of the present invention have been describedusing a particular combination of hardware and software, it should berecognized that other combinations of hardware and software are alsowithin the scope of the present invention. Embodiments of the presentinvention may be implemented only in hardware, or only in software, orusing combinations thereof.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that additions, subtractions, deletions, and other modificationsand changes may be made thereunto without departing from the broaderspirit and scope as set forth in the claims.

Other variations are within the spirit of the present invention. Thus,while the invention is susceptible to various modifications andalternative constructions, certain illustrated embodiments thereof areshown in the drawings and have been described above in detail. It shouldbe understood, however, that there is no intention to limit theinvention to the specific form or forms disclosed, but on the contrary,the intention is to cover all modifications, alternative constructions,and equivalents falling within the spirit and scope of the invention, asdefined in the appended claims.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the invention (especially in the context of thefollowing claims) are to be construed to cover both the singular and theplural, unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising,” “having,” “including,” and “containing”are to be construed as open-ended terms (i.e., meaning “including, butnot limited to,”) unless otherwise noted. The term “connected” is to beconstrued as partly or wholly contained within, attached to, or joinedtogether, even if there is something intervening. Recitation of rangesof values herein are merely intended to serve as a shorthand method ofreferring individually to each separate value falling within the range,unless otherwise indicated herein, and each separate value isincorporated into the specification as if it were individually recitedherein. All methods described herein can be performed in any suitableorder unless otherwise indicated herein or otherwise clearlycontradicted by context. The use of any and all examples, or exemplarylanguage (e.g., “such as”) provided herein, is intended merely to betterilluminate embodiments of the invention and does not pose a limitationon the scope of the invention unless otherwise claimed. No language inthe specification should be construed as indicating any non-claimedelement as essential to the practice of the invention.

Preferred embodiments of this invention are described herein, includingthe best mode known to the inventors for carrying out the invention.Variations of those preferred embodiments may become apparent to thoseof ordinary skill in the art upon reading the foregoing description. Theinventors expect skilled artisans to employ such variations asappropriate, and the inventors intend for the invention to be practicedotherwise than as specifically described herein. Accordingly, thisinvention includes all modifications and equivalents of the subjectmatter recited in the claims appended hereto as permitted by applicablelaw. Moreover, any combination of the above-described elements in allpossible variations thereof is encompassed by the invention unlessotherwise indicated herein or otherwise clearly contradicted by context.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to the sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

What is claimed is:
 1. A method comprising: under control of one or morecomputer systems configured with executable instructions: causingprovision of graphical objects to a user through an interface, thegraphical objects representative of semantic objects; detecting anarrangement of a subset of the graphical objects, the arrangement atleast partially defining a policy and logic for an analysis to beperformed as part of implementing the policy; generating, based at leastin part on the arrangement, executable instructions for execution by anapplication configured to operate according to the instructions toreason transactional data according to the policy, wherein the policyspecifies of a set of conditions and the reasoning comprises analyzingthe transactional data resulting from future transactions forfulfillment and/or violation of the set of conditions; reasoning dataaccording to the policy with a plurality of reasoners, the reasoning thedata comprising: directing a semantic reasoner of the plurality ofreasoners to operate to produce a first set of inferences, and directinga pattern-based reasoner of the plurality of reasoners to operate toproduce a second set of inferences; and creating a set of resultsindicative of results of the reasoning, the set of results based atleast in part on the first set of inferences and the second set ofinferences.
 2. The method as recited in claim 1, where the reasoning thedata according to the policy with the plurality of reasoners comprisesdirecting the semantic reasoner and the pattern-based reasoner tooperate in series so that the pattern-based reasoner produces the secondset of inferences based at least in part on results corresponding to thefirst set of inferences produced by the semantic reasoner.
 3. The methodas recited in claim 1, where the reasoning the data according to thepolicy with the plurality of reasoners comprises directing the semanticreasoner and the pattern-based reasoner to operate in series so that thesemantic reasoner produces the first set of inferences based at least inpart on results corresponding to the second set of inferences producedby the pattern-based reasoner.
 4. The method as recited in claim 1,wherein the set of results indicates the fulfillment and/or theviolation of the set of conditions based at least in part on monitoringthe transactional data.
 5. The method as recited in claim 1, furthercomprising: causing provision of an analytic object representative of ananalysis to be performed as part of implementing the policy; and whereinthe arrangement is based at least in part on an association of theanalytic object representative of the analysis with at least the subsetof the graphical objects.
 6. The method as recited in claim 1, furthercomprising: receiving a selection through the interface along inconjunction with the detection of the arrangement, where the selectioncorresponds to a selection of the semantic reasoner of the plurality ofreasoners and the directing the semantic reasoner is consequent to theselection.
 7. The method as recited in claim 1, further comprising:receiving a selection through the interface along in conjunction withthe detection of the arrangement, where the selection corresponds to aselection of the pattern-based reasoner of the plurality of reasonersand the directing the pattern-based reasoner is consequent to theselection.
 8. A system comprising: one or more processorscommunicatively coupled to memory, the one or more processors to: causeprovision of graphical objects to a user through an interface, thegraphical objects representative of semantic objects; detect anarrangement of a subset of the graphical objects, the arrangement atleast partially defining a policy and logic for an analysis to beperformed as part of implementing the policy; generate, based at leastin part on the arrangement, executable instructions for execution by anapplication configured to operate according to the instructions toreason transactional data according to the policy, wherein the policyspecifies of a set of conditions and the reasoning comprises analyzingthe transactional data resulting from future transactions forfulfillment and/or violation of the set of conditions; reason dataaccording to the policy with a plurality of reasoners, the reasoning thedata comprising: directing a semantic reasoner of the plurality ofreasoners to operate to produce a first set of inferences, and directinga pattern-based reasoner of the plurality of reasoners to operate toproduce a second set of inferences; and create a set of resultsindicative of results of the reasoning, the set of results based atleast in part on the first set of inferences and the second set ofinferences.
 9. The system as recited in claim 8, where the reasoning thedata according to the policy with the plurality of reasoners comprisesdirecting the semantic reasoner and the pattern-based reasoner tooperate in series so that the pattern-based reasoner produces the secondset of inferences based at least in part on results corresponding to thefirst set of inferences produced by the semantic reasoner.
 10. Thesystem as recited in claim 8, where the reasoning the data according tothe policy with the plurality of reasoners comprises directing thesemantic reasoner and the pattern-based reasoner to operate in series sothat the semantic reasoner produces the first set of inferences based atleast in part on results corresponding to the second set of inferencesproduced by the pattern-based reasoner.
 11. The system as recited inclaim 8, where the set of results indicates the fulfillment and/or theviolation of the set of conditions based at least in part on monitoringthe transactional data.
 12. The system as recited in claim 8, the one ormore processors further to: cause provision of an analytic objectrepresentative of an analysis to be performed as part of implementingthe policy; and where the arrangement is based at least in part on anassociation of the analytic object representative of the analysis withat least the subset of the graphical objects.
 13. The system as recitedin claim 8, the one or more processors further to: process a selectionreceived through the interface along in conjunction with the detectionof the arrangement, where the selection corresponds to a selection ofthe semantic reasoner of the plurality of reasoners and the directingthe semantic reasoner is consequent to the selection.
 14. The system asrecited in claim 8, further comprising: process a selection receivedthrough the interface along in conjunction with the detection of thearrangement, where the selection corresponds to a selection of thepattern-based reasoner of the plurality of reasoners and the directingthe pattern-based reasoner is consequent to the selection.
 15. One ormore non-transitory, machine-readable media having machine-readableinstructions thereon which, when executed by one or more processors,cause the one or more processors to perform: causing provision ofgraphical objects to a user through an interface, the graphical objectsrepresentative of semantic objects; detecting an arrangement of a subsetof the graphical objects, the arrangement at least partially defining apolicy and logic for an analysis to be performed as part of implementingthe policy; generating, based at least in part on the arrangement,executable instructions for execution by an application configured tooperate according to the instructions to reason transactional dataaccording to the policy, wherein the policy specifies of a set ofconditions and the reasoning comprises analyzing the transactional dataresulting from future transactions for fulfillment and/or violation ofthe set of conditions; reasoning data according to the policy with aplurality of reasoners, the reasoning the data comprising: directing asemantic reasoner of the plurality of reasoners to operate to produce afirst set of inferences, and directing a pattern-based reasoner of theplurality of reasoners to operate to produce a second set of inferences;and creating a set of results indicative of results of the reasoning,the set of results based at least in part on the first set of inferencesand the second set of inferences.
 16. The one or more non-transitory,machine-readable media as recited in claim 15, where the reasoning thedata according to the policy with the plurality of reasoners comprisesdirecting the semantic reasoner and the pattern-based reasoner tooperate in series so that the pattern-based reasoner produces the secondset of inferences based at least in part on results corresponding to thefirst set of inferences produced by the semantic reasoner.
 17. The oneor more non-transitory, machine-readable media as recited in claim 15,where the reasoning the data according to the policy with the pluralityof reasoners comprises directing the semantic reasoner and thepattern-based reasoner to operate in series so that the semanticreasoner produces the first set of inferences based at least in part onresults corresponding to the second set of inferences produced by thepattern-based reasoner.
 18. The one or more non-transitory,machine-readable media as recited in claim 15, where the set of resultsindicates the fulfillment and/or the violation of the set of conditionsbased at least in part on monitoring the transactional data.
 19. The oneor more non-transitory, machine-readable media as recited in claim 15,wherein the machine-readable instructions further cause the one or moreprocessors to perform: causing provision of an analytic objectrepresentative of an analysis to be performed as part of implementingthe policy; and where the arrangement is based at least in part on anassociation of the analytic object representative of the analysis withat least the subset of the graphical objects.
 20. The one or morenon-transitory, machine-readable media as recited in claim 15, whereinthe machine-readable instructions further cause the one or moreprocessors to perform: processing a selection received through theinterface along in conjunction with the detection of the arrangement,where the selection corresponds to a selection of the semantic reasonerof the plurality of reasoners and the directing the semantic reasoner isconsequent to the selection.